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The premier event dedicated to research, analytics, and application of Bayesian networks.
The biannual BayesiaLab conference stands out as the premier global event dedicated to research, analytics, and application of Bayesian networks. What started in 2013 in Orlando, Florida, as a modest gathering has now blossomed into a global event, drawing attendees from various corners of the world. 2022 marked an unforgettable chapter for us as we celebrated the 10th Annual BayesiaLab Conference. Reflecting the growing interest in our subjects, we've decided to transition from an annual to a biannual schedule.
Understanding Bayesian Networks in Action
Judea Pearl introduced the world to Bayesian networks in the 1980s. Since then, their mathematical and statistical strengths have been embraced in computer science. Yet, it's only of late that their immense capabilities as a versatile research tool, extending beyond just computer science, have come to the forefront. The primary aim of our conference is to champion the practical deployment of Bayesian networks across research, analytics, and problem-solving.
Join us and dive deep into the myriad practical applications of Bayesian networks, spanning from architecture to zoology and everything in between. A highlight for many returning attendees is the conference's interdisciplinary nature. For instance, one might discover that a groundbreaking medical research method might also be a perfect fit for a challenge in marketing science and vice versa.
Embracing the BayesiaLab Spirit
From its inception in Orlando in 2013, this conference has become more than just another academic gathering. Over the years, it has fostered a diverse community comprising researchers and professionals from various domains. This mix often results in unexpected partnerships, like military planners teaming up with market analysts. Given the evolving nature of Bayesian networks, nearly all attendees are in a continuous learning process, with only a handful considering themselves specialists. So, if you're a newcomer, you'll fit right in as we embark on this enlightening journey together.
Watch hundreds of hours of presentation videos from BayesiaLab Conferences since 2013.
October 11–12, 2024 – A Zoom Virtual Event
The BayesiaLab E-Summit is the new format of what used to be the BayesiaLab Fall Conference. Post-COVID, we have established a new routine for our biannual events. Each spring, our community will meet face-to-face at the BayesiaLab Conference, like we did in the spring of 2024 in Cincinnati, and every fall, we will gather online at the BayesiaLab E-Summit.
In either format, these meetings stand out as the premier events dedicated to applied research with Bayesian networks. They aim to promote the practical deployment of Bayesian networks in research, analytics, and problem-solving.
What started in 2013 in Orlando, Florida, as a modest gathering has now blossomed into an international event, drawing attendees from various corners of the world. Over the years, our conferences and virtual events have fostered a vibrant community comprising researchers and professionals from many domains.
t.b.a.
Gabriel Andraos, Voya Financial
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
This presentation picks up where my previous one ended (From Explanations to Interpretations at the 2022 BayesiaLab Conference). Back then, I described our approach to optimize the strengths of machines and humans through interpretable AI. This time, we will focus on the key elements that can make this a winning vs. losing combination (hint: not trivial and not obvious). We will explore the benefits of a neuro-symbolic approach and conclude with a proposed framework for optimal collaborative intelligence.
Gabriel Andraos jointly leads Voya’s Machine Intelligence group (VMI). As the co-head of VMI, he focuses on research and development in the application of AI and machine learning models for fundamental investing. Prior to this role, Gabriel was a managing partner and co-founder of G Squared Capital LLP, which was acquired by Voya in 2020. For more than 12 years - at G Squared and at Voya – the team has been running virtual employees – analysts, traders, and portfolio managers with transparent, explainable computer models anchored in fundamentals. Before that, Gabriel held senior investment roles in Europe, the U.S., and Asia, combining knowledge and experience in fundamental analysis with the latest tools in computing and data science. Gabriel received an MBA from Harvard Business School and a BA in Economics from Georgetown University. He also has a Certificate in Quantitative Finance and several artificial intelligence, data science, and machine learning accreditations.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Dr. Lionel Jouffe, Bayesia S.A.S.
Recorded at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
This presentation explores the revolutionary potential of Hellixia, BayesiaLab's specialized assistant, for knowledge discovery and decision support using large-scale language models (LLMs) such as GPT-4.
Hellixia presents itself as a significant innovation, exploiting the wealth of knowledge embedded in LLMs to identify and suggest the most relevant dimensions of a field of study (a process akin to crowdsourcing).
Hellixia uses user-selected keywords (such as causes, levers, effects, concepts, forces, and ideas) to generate mathematical representations capturing the semantics (embeddings), enabling automatic learning of semantic Bayesian networks with BayesiaLab.
Hellixia doesn't limit itself to semantics; it can go further by testing causal hypotheses and automatically generating causal networks, thus providing invaluable help in analyzing and understanding complex relationships within the domains under study.
This ability to automatically translate latent knowledge into semantic and causal networks opens up new avenues for knowledge extraction and decision support, marking a significant advance in knowledge extraction and decision support, marking a significant advance in exploiting the capabilities of LLMs for a wide range of fields, including marketing, industry, medicine, economics, politics, literature, and even philosophy.
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
Join us at the BayesiaLab E-Summit and bring to life your work with Bayesian networks on our virtual stage. The 2024 E-Summit promises rich insights into the latest in applied research using probabilistic graphic models and Bayesian networks. Besides, through enlightening courses and talks, immerse yourself in best practices with the BayesiaLab software platform.
Your platform choice, whether BayesiaLab or another, doesn't restrict your participation. We welcome entries from academia, government, industry, and solution providers, focusing on new applications, ongoing work, and capabilities within Bayesian networks.
Date: October 11–12, 2024 Slot Duration: 30–45 minutes (including 5–15 minutes of Q&A)
Title
Presenter's name & affiliation
Abstract (max. 300 words)
High-resolution presenter photos (min. 500x500 pixels)
Presenter biography (max. 100 words per presenter)
Proposed format (e.g., PowerPoint, Keynote, Prezi)
Accepted abstracts will be showcased on the Bayesia website before the E-Summit. All presentations will be recorded. By submitting, you consent to your presentation and its recording being shared on the Bayesia website and related social media platforms.
Direct your presentation proposals to conference@bayesia.us.
If you are presenting at a BayesiaLab meeting for the first time, please check out the archives from previous online events and in-person conferences.
Martin Block, Ph.D., Northwestern University
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
A Bayesian Media Influence Network solves several problems with a traditional regression-based media marketing mix model. First is the problem of consistent measures across different media types. This is solved by using syndicated survey media and marketing influence measures. Second is the problem of simultaneous consumption and the assumption of independence among predictor variables. Third is the problem of non-linear relationships that may exist between media types and a criterion variable such as sales. A Bayesian Belief Network solves these last two problems and provides an easy-to-understand tool to aid in what has been a traditionally difficult marketing asset allocation decision. Using women’s apparel as an example, the efficacy of the Bayesian Network for monthly spending is shown, identifying a pattern of media types. Factoring past brand purchase behavior into segments shows how the Bayesian Network can be used to target High-End brands, Accessories, and Active Shoes as examples. Other product categories and target definitions can certainly be used.
Martin Block is a Professor Emeritus in Integrated Marketing Communications at Northwestern University and a Director of the Retail Analytics Council. Prior to 1985, he was Professor and Chairperson of the Department of Advertising at Michigan State University. Prior to that, he worked as a Senior Market Analyst in Corporate Planning at the Goodyear Tire and Rubber Company. Co-author of Understanding China’s Digital Generation, Media Generations: Media Allocation in a Consumer-Controlled Marketplace, Retail Communities: Customer Driven Retailing, Analyzing Sales Promotion, and Business-to-Business Market Research. He has published in many academic research journals and trade publications and has several book chapters. Paul has a Ph.D. from Michigan State.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Hana C. Long, Ph.D., NC State University
Presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
To better manage emerging public health challenges, communities, water utilities, and regulators must improve methods to assess and prioritize environmental health risks. PFAS exposure in North Carolina is a prime example where methodologies for predicting and mitigating risks are developing concurrently with contaminant regulations and the allocation of mitigation funding. This project aims to predict the risk of private wells exceeding the provisional health goal for the PFAS GenX. PFAS compounds are notoriously difficult to model with mechanistic groundwater flow and fate and transport models. The sheer number of different PFAS chemicals and uncertainty in their individual and interacting characteristics all make them complex to model in the environment. Mechanistic models are also resource-intensive to develop and calibrate. This project builds upon previous work that developed a Machine-Learned Bayesian Network (MLBN) classification model to predict at-risk wells; current work integrates outputs from a mechanistic groundwater fate and transport model as input variables to new MLBNs, classified as low-, medium-, and high-effort models in terms of mechanistic modeling resources required. The performance of each model is compared to the mechanistic model predictions of at-risk wells using several performance metrics, including accuracy, area under the receiver operating characteristic curve (AU-ROC), and F-score curves, and the importance of each metric and model performance is discussed in the context of environmental health risks. Results show that MLBNs perform as well as the mechanistic models in accuracy and AU-ROC performance metrics while being more robust in terms of the range of decision thresholds selected for risk classification. High-effort models make slight improvements in AU-ROC metrics while more easily incorporating insights from mechanistic model performance without the need to recalibrate the mechanistic model. The project aims to assist regulators in advancing public health and methodologies to integrate traditional engineering models with machine-learning approaches.
Hana C. Long is a postdoctoral researcher in the Department of Civil, Construction, and Environmental Engineering at NC State University (NCSU). Her research uses mathematical optimization and statistical modeling to help communities make sustainable and resilient infrastructure decisions. Hana holds a PhD in Operations Research from NCSU. She previously worked as a project engineer in the Wastewater Research Group at the Los Angeles County Sanitation Districts and with the Community Resilience Group at the National Institute of Standards and Technology. She holds a Master's in Civil Engineering (NCSU) and a Bachelor's degree in Mathematics and Russian Language (Vanderbilt University).
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
The BayesiaLab community is excited to meet again in person after four years of virtual conferences.
Graduate Hotel Cincinnati, 151 Goodman Drive, Cincinnati, Ohio 45219 April 11–12, 2024
The biannual BayesiaLab conference stands out as the premier event dedicated to applied research with Bayesian networks. Our conference aims to promote the practical deployment of Bayesian networks in research, analytics, and problem-solving.
What started in 2013 in Orlando, Florida, as a modest gathering has now blossomed into an international event, drawing attendees from various corners of the world. Over the years, it has fostered a vibrant community comprising researchers and professionals from many domains.
For questions about the conference, please reach out to us anytime:
Toll-Free: (888) 386-8383 #3
International: +1 615 988-7738 #3
Email: conference@bayesia.us
In conjunction with our conference, we will be offering introductory and advanced BayesiaLab courses. Join scientists from around the world to learn how to apply Bayesian networks to your research.
To get an idea of what a typical BayesiaLab Conference looks like, you can watch the recorded presentations from previous years.
Kurt S. Schulzke, JD, CPA, CFE, University of North Georgia
Presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
In response to a lawsuit brought in 2013 by Maximilian Schrems, on May 12, 2023, the Irish Data Protection Commission (DPC) fined Meta Ireland (formerly Facebook) €1.2 billion for violating the EU's General Data Protection Regulation (GDPR) through its transfers to the United States of Facebook users' personal data. These transfers were found by EU authorities to violate Chapter V of the GDPR and the EU Charter of Fundamental Rights because, upon transfer, the data becomes accessible to the U.S. Government through the FISA 702 PRISM program and its successors. In addition to the fine, the DPC also ordered Meta Ireland to suspend further transfers of data to the United States. In the wake of this DPC decision, Meta Ireland must choose how to respond. Options include complying with the decision (i.e., suspending further data transfers), closing Meta's EU business, or violating the order. It is also possible that the DPC's decision might be overturned on appeal, in which case Meta could continue operating as it does now, transferring customer data to the United States with impunity. Meta's choice must be made in the face of considerable uncertainty and will impact future EU enforcement actions (e.g., more fines), as well as Meta's future EU-related revenues and expenses. This presentation models and optimizes Meta's choice using Bayesian networks and influence diagrams and illustrates how to deal with "functional asymmetry" in designing influence diagrams.
Kurt Schulzke, JD, CPA, CFE, is a Professor of Accounting & Law at the University of North Georgia. His teaching, research, and consulting thrive at the intersection of data science, accounting, law, and risk management. He has published in the Columbia Journal of Transnational Law, Vanderbilt Journal of Transnational Law, Tennessee Journal of Business Law, Journal of Forensic Accounting Research, and The Value Examiner. MAcc (Brigham Young University), J.D. (Georgia State University), M.S. Applied Statistics (Kennesaw State University).
Krishna Ganta, Rohit Warrier, Riley Mulhern, Ted Lillys, Jennifer Hoponick Redmon, Jacqueline MacDonald Gibson
Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
Research Triangle Institute International, Research Triangle Park, NC, USA
Brown & Caldwell, Englewood, CO, US
Vikram Suresh, Ph.D., University of Cincinnati
Presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
Using Bayesian Belief Networks (BBN), variables from three graphs based on Relative Preference Theory (RPT) were integrated. The graphs in RPT have analogies to known graphs in other frameworks for human judgment but are derived from the only framework tested and found to meet physics criteria for lawfulness. The graphs are analogous to those found in Prospect Theory, Markowitz portfolio theory, and the Payne, Bettman, and Johnson adaptive strategy selection. RPT produces nonlinear graphs with R2 fits per person, on average > 0.9. RPT graphs calibrate value based on patterns in prior judgments (value function), limits in risk-reward and risk-aversion relations (limit function), and balance between reward and aversion judgments (tradeoff function). Using the same picture rating task administered to three distinct internet cohorts, each with more than 3,000 subjects, graphs were observed calibrating reward/aversion value (value function), associating risk to reward/aversion (limit function), and balancing reward against aversion (tradeoff function). Fifteen mathematical features of these graphs based on engineering approaches or behavioral finance constructs, such as loss aversion and risk aversion, were computed and then used as input for Bayesian Belief Network analysis and correlational analysis to identify consistent relationships between these fifteen mathematical features. When consistent relationships were observed, we then fit mathematical functions to the combined dataset of 10,000+ subjects. This analysis showed that the calibration of value (prospect theory-like graph) anchors the relationship of risk-reward variables in a distinct manner from how it anchors the relationship of risk to aversion, and four distinct clusterings of these graph features can be observed based on their graph of origin with highly interpretable relationships. The fifteen graph features can be combined and applied to predict the outputs of human judgment using machine learning. Thus, it is possible to create an individual profile or “fingerprint,” which can be used to predict behavior or other psychological conditions such as anxiety, depression, and suicidality. Marketers can use the features as a segmentation variable to identify the best prospects and design the most effective messages.
Hans Breiter
and Martin Block, Shamal Lalvani, Sumra Bari, Nicole L. Vike, Leandros Stefanopoulos, Byoung-Woo Kim, Aggelos K. Katsaggelos
Medill Integrated Marketing Communications, Northwestern University, Evanston, IL, USA
Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
Department of Computer Science, University of Cincinnati, Cincinnati, OH, USA
School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
Department of Radiology, Northwestern University, Chicago, IL, USA
Department of Computer Science, Northwestern University, Evanston, IL, USA
Laboratory of Neuroimaging and Genetics, Department of Psychiatry, Massachusetts General Hospital and Harvard School of Medicine, Boston, MA, USA
Vikram Suresh, Ph.D., has been a Postdoctoral Fellow in the Department of Computer Science at the University of Cincinnati since August 2023. He earned his Ph.D. in Business Administration (Quantitative Economics) from the University of Cincinnati in 2023. His research focuses on combining Bayesian hierarchical modeling, AI, and econometric methods to analyze various topics, including treatment outcomes in adolescents with depression, socioeconomic predictors of treatment outcomes in adults with major depressive disorder, and the impact of age on antidepressant response. He has several publications in peer-reviewed journals and is working on manuscripts related to statistical approaches in randomized controlled trials, income inequality estimation, and the decline in high school student performance using AI experiments.
Rahul Pandey & Anand Wilson, Course5 Intelligence
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 11, 2024.
This study delves into the effectiveness of an integrated, weighted approach in influencing strategic brand decisions, highlighting the constraints of traditional machine learning and data science techniques that rely heavily on large, clean datasets and struggle with complex variable interrelations. By adopting Bayesian structural equation modeling, our research leverages prior knowledge, expert insights, and beliefs, focusing particularly on how various metrics impact market share. Through primary search surveys across different market segments, we assess brand perception, aggregating data from numerous sources over a set timeframe for comprehensive analysis.
The methodology begins with exploratory data analysis, leading to the testing of probabilistic associations to validate hypotheses and develop the model. An unsupervised network graph forms the core of our model, illustrating the interactions between variables from disparate data sources at a specific time. This base model undergoes scrutiny for statistical robustness and output validation, considering both total and direct effect values to determine weighted coefficients. Further refinement of the network model incorporates business and market inputs, ensuring the model's assumptions align with real-world conditions without undermining its stability. This strategic methodology offers nuanced, actionable insights into a brand's market position, enhancing adaptability and strategic decision-making as new information emerges.
Rahul Pandey, AVP Digital and Advanced Analytics at Course5 Intelligence
Rahul is an Applied AI and Data Science leader, experienced in setting up and scaling multi-disciplinary global high-performance applied data science teams in India and the US. Rahul is recognized as LinkedIn's top voice for – Data Science, Artificial Intelligence, GenAI, and Leadership. Rahul has also been awarded twice as “40 under 40 data scientists” in India consecutively in 2023 and 2024. In his current role, Rahul heads the ‘Global Data Science Practice’ at Course5i and has been instrumental in scaling data science practice and solutions at course5i. He has developed multiple solutions, written papers on applications of Generative AI, and presented them at conferences across the globe. He specializes in creating data science strategies to solve problems through thought leadership and the application of advanced algorithms for executive leaders in Fortune 100 and Fortune 500 companies across verticals. He has also developed unanimous trust among industry stalwarts for solving problems that reflect business value.
Anand Wilson, Senior Data Science Consultant, Advanced Analytics and Applied AI, Course5 Intelligence
Anand has over 11 years of experience in applied artificial intelligence and data sciences. He creates market solutions based on Bayesian Network theory, which can quantify causality in observational studies. His work and research areas include Knowledge Modelling and Machine Learning with BayesiaLab. Anand has a background in applied statistics and a keen interest in machine reasoning, causal inference, and experimental design.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Fathien Azuien Yusriza, Universiti Kuala Lumpur, Malaysian Institute of Aviation Technology
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
This study addresses the crucial purpose of enhancing supply chain (SC) efficiency and effectiveness in the face of unforeseen disruptions, particularly examining the impact of the COVID-19 pandemic on the airline catering sector. With a focus on SC performance assessment, the researchers propose and implement Bayesian network (BN) modeling as a strategic tool to measure and quantify the consequences of pandemic disruptions. The study employs forward and backward propagation analysis within the BN model to simulate and measure the impact of different triggers on SC performance and business continuity. The findings provide a valuable theoretical perspective on the use of BNs in pandemic SC disruption modeling, offering insights that can serve as a decision-making tool for predicting and comprehending the effects of pandemics on SC performance. This research contributes to a better understanding of SC dynamics in the context of unforeseen disruptions and provides a foundation for informed decision-making in the airline catering sector amid uncertainties.
Fathien Azuien Yusriza is a highly skilled professional with a Bachelor's Degree in Aviation Management from Universiti Kuala Lumpur, Malaysian Institute of Aviation Technology (UniKL MIAT), and a Master's degree in Engineering Technology (Aerospace). With a rich background, she has contributed her expertise to global logistics companies, including DHL APSSC and Airbus Malaysia, as a transport analyst. Currently engaged in research focusing on the effectiveness of aviation's supply chain management, she actively contributes to scholarly publications, including journal articles and book chapters, showcasing her commitment to advancing knowledge in the field.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Yong Zhang, Ph.D., Procter & Gamble
We have been exploring different ways to leverage GenAI and causal inference to disrupt the speed, scope, and economics of product innovation at P&G. This includes LLM like ChatGPT and Mixtral_8X7B, open source packages like Langchain and LlamaIndex and third-party software/module Hellixia. We use these existing tools or build new tools on top of them to understand consumers and drive product innovation. We leverage these tools to generate structure summaries and reports from large volumes of data and to generate qualitative or semiquantitative structural causal models, etc.
Dr. Yong Zhang leverages Bayesian data and modeling science to develop a strategy for product design, manufacturing, storage, and transportation across P&G to improve consumers’ quality of life and drive positive influence on the environment and society under different climate change scenarios. He develops modeling and simulation methods and tools through Front End Innovation projects to enable and promote the capability across P&G for breakthrough consumer understanding and product innovation. The methods and tools can be used to extract and integrate information from a variety of data sources to find a “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.
Recorded at the in Cincinnati on April 12, 2024.
Abraham Rojas Zuniga, Curtin University
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
Duplex stainless steel (DSS) alloys, recognised for their high mechanical strength and corrosion resistance, are increasingly utilised in the oil and gas industry to mitigate critical degradation risks in downhole environments. Elevated pressures, temperatures, and corrosive agents such as chlorides, carbon dioxide, and hydrogen sulphide characterise these settings. Collectively, these factors contribute to various environmentally assisted cracking mechanisms, predominantly stress corrosion cracking (SCC). This corrosion phenomenon significantly threatens production systems, potentially causing premature failures of metallic materials due to the synergistic effects of tensile stresses and corrosive media. Despite the growing adoption of DSS alloys, their performance in oil and gas applications remains inadequately understood within existing standards, rendering the operational boundaries of DSS perceived as overly conservative. While comprehensive research has explored DSS's resistance to SCC, the reliable assessment of SCC risks in the field remains a significant challenge. Consequently, there is a need for a framework to evaluate, with reasonable certainty, the viability of DSS applications in production systems. We address these limitations by introducing a data-centric approach through Bayesian networks (BNs) for assessing the SCC risks of DSS in downhole environments. We developed this BN model by combining various information sources, including industry standards, technical guidelines, and scientific papers. We used advanced pre-processing techniques, such as data imputation and synthetic minority oversampling, to prepare the dataset adequately. Furthermore, the BN model's structure and predictive accuracy were also compared with other modelling methods, such as XGBoost and SHAP analysis, which provide additional insights into the causality of SCC. More importantly, our BN model demonstrates that the SCC resistance of DSS alloys can comfortably exceed the operational threshold established in standards, currently within 0.02 – 0.2 bar of the partial pressure of hydrogen sulphide.
Abraham Rojas Zuniga abraham.rojaszuniga@postgrad.curtin.edu.au
Sam Bakhtiari sam.bakhtiari@curtin.edu.au
Ke Wang ke.wang2@curtin.edu.au
Chirs Aldrich chris.aldrich@curtin.edu.au
Victor M. Calo victor.calo@curtin.edu.au
Mariano Iannuzzi mariano.iannuzzi@alcoa.com
Curtin Corrosion Centre, Faculty of Science and Engineering, Curtin University.
Western Australian School of Mines: Minerals, Energy and Chemical Engineering, Faculty of Science and Engineering, Curtin University.
Computing and Mathematical Sciences, Faculty of Science and Engineering, Curtin University.
Abraham Rojas Zuniga
As a petroleum engineer with five years of experience, I have advanced my academic career with a Master's degree (M.Phil.) in Oil and Gas Engineering from the University of Western Australia. As a Ph.D. candidate at Curtin University, my research focuses on chemical engineering and artificial intelligence to improve our understanding of corrosion phenomena in hydrocarbon industry alloys. I am keenly interested in applying simulation techniques, ranging from deterministic models to data-driven methods, to investigate material science phenomena and enhance risk assessment strategies.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Alexander Alexeev, Ph.D. & Rafael Reuveny, Ph.D., Indiana University - Bloomington
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
Although in multiple episodes, Weather Disasters (WDs) are found to be potentially important factors promoting migration between countries and regions, it is unclear if their role is systematic or idiosyncratic. This issue is of great public attention and importance since the severity, frequency, and coverage of WDs are expected to grow as climate change progresses under business as usual. Recent studies suggest that large migration flows may be associated with violence, economic and demographic factors in the countries of origin while paying less attention to the effect of socio-economic conditions in the destination countries. Our research has twofold objectives. First, this study develops a Bayesian Network Analytic (BNA) framework that anticipates the potential for varied migration responses to WDs across countries and over time, and examines policy levers that might alter these responses, the complex interaction between different factors, and overcomes some limitations of econometric models routinely used in modeling of international migration. The network structure is learned from data for migration flows between 190 origins and 190 destinations from 1980 to 2009. Secondly, we compare and discuss the advantages, disadvantages, and results of both BNA and conventional econometric modeling, which were previously done on the same data.
Alexander Alexeev earned a Ph.D. in Public Affairs from Indiana University in 2010, with specializations in policy analysis and business economics. He also holds a Ph.D. in Physics from Odessa National University (Ukraine, 1996). Starting in 1997, Alexander taught physics, modeling, and radioecology for the Department of Physics at Odessa Hydrometeorological Institute. In 2001, he came to the United States to study environmental management and stayed for doctoral study. Since 2017, Alexeev has been a lecturer at Indiana University, teaching data analysis and statistical modeling courses. His interdisciplinary research interests include quantitative policy analysis, risk and security modeling, and decision-making.
Ray Niaura, Ph.D. & Shu Xu, Ph.D., New York University
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
There are approximately 1.1 billion tobacco smokers in the world who either suffer from or are at risk of smoking-related diseases such as cancers and cardiorespiratory conditions. Smokers who quit, especially at earlier ages, will gain back several years of life from smoking-related premature mortality. Quitting smoking, however, is difficult despite the availability of evidence-based quit methods, including medications and behavioral counseling. Electronic cigarettes (e-cigs) are being used by an increasing number of smokers, and the question arises whether e-cigs can help tobacco smokers quit. Data from randomized controlled trials (RCTs) suggests that e-cigs can serve as a substitute for cigarettes and are more effective than nicotine replacement therapies (e.g., nicotine patches, gum, lozenges) for smoking cessation. Data from observational studies are less clear about the association between the use of e-cigs and quitting smoking. We present results from two nationally representative US survey studies in which we examine factors that are associated with cigarette smoking and the use of e-cigs and whether the use of e-cigs is associated with quitting smoking (National Health Interview Survey – NHIS; Population Assessment of Tobacco and Health – PATH). Unsupervised BN learning of the 2022 NHIS survey showed a clustering of factors normally associated with cigarette smoking, including poverty and educational levels, depression, anxiety, disability, alcohol consumption, sexual orientation, nativity, and veteran status. There were only two direct paths from educational attainment and nativity to smoking status. E-cig use was directly associated with smoking status and age, suggesting that the determinants of use differ somewhat between cigarettes and e-cigs. Longitudinal data from the PATH study set up as a cross-lagged BN model, showed negative associations between cigarette and e-cig use at each successive year across 6 years, indicating a cumulative impact of e-cig use on smoking cessation. We will discuss challenges and options regarding longitudinal data analysis via Bayesian networks.
Dr. Niaura is a Professor of Social and Behavioral Sciences and Epidemiology and Chair of the Department of Epidemiology at the School of Global Public Health, New York University. From 2009-2017, he was Director of Research at the Schroeder Institute, Truth Initiative (formerly the Legacy Foundation) in Washington, DC. He has extensive expertise in tobacco dependence and treatment, and he has published over 400 peer-reviewed articles and several book chapters in this area. His interests include studying the biobehavioral substrates of tobacco dependence, evaluating behavioral and pharmacological treatments for cessation, and understanding and addressing public health disparities in tobacco-related burdens of illness and disability. He has been the Principal Investigator (PI) or co-investigator of over 70 NIH-funded grants, and he is the former President of the Society of Nicotine and Tobacco Research. He is currently a co-I of a large, multicenter initiative: the Population Assessment of Tobacco and Health Study (PATH, funded by the National Institute on Drug Abuse/Center for Tobacco Products, FDA), a national, longitudinal cohort study of more than 40,000 users and non-users of tobacco products ages 12+, including adolescents and young adults.
Shu (Violet) Xu, Ph.D. sx5@nyu.edu Clinical Assistant Professor Department of Biostatistics School of Global Public Health New York University
My work represents a balance of both statistical and applied aspects of quantitative methodology. My primary quantitative interests include evaluating and developing statistical methods for longitudinal data analysis. Specifically, My research focuses on various aspects of latent growth models, missing data methods, and causal inference models.
I have served as an Investigator/Biostatistician on more than 10 federally or locally funded research projects. I was PI of an NIH/NCI supplement award through the University of Michigan/Georgetown Center for the Assessment of the Public Health Impact of Tobacco Regulations (3U54CA229974), and the project aimed to examine the longitudinal effect of e-cigarette exposure on subsequent tobacco use patterns using conventional and causal mediation methods. I was also a co-investigator of an NIH/NCI R21 award (1R21CA260423-01). This project aims to assess the longitudinal impact of e-cigarette flavor, device, and marketing exposure on tobacco use and health outcomes using propensity score weighting and causal mediation methods. I am PI of an on-going NIH NIDA/FDA K01 award (1K01DA058408). This project aims to develop and implement causal machine-learning methods to inform tobacco regulatory sciences.
I have collaborated with substance use, family, and health researchers to advance and share my knowledge of quantitative methodology and pursue a better understanding of the social sciences and public health. I have conducted research with the Family Translational Research Group at New York University and the Methodology Center at Pennsylvania State University.
Emmanuel Keita, Sundiata
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
I propose a 3,000-year journey through time and space, from the earliest traces of oracular practices and the I Ching to the current use of so-called “generative (probabilistic) artificial intelligence (modeling)” with Bayesialab as a fabulous pedagogical tool.
Not forgetting God and a game of dice, this talk will question our perceptions of the world, our relationship to chance, and, by the way, our attitude to the eternal question of how to make (good...) decisions as humans in an ever-changing world 易!
Emmanuel KEITA is a consultant, keynote speaker, and trainer.
An advocate of "being more human in a woLRd of machines” he offers a unique perspective on decision-making at the intersection of technology (AI, BI, MDM, Process Mining, etc.), cognitive science, and traditional knowledge.
Coaching anyone facing stressful performance, fatigue, or motivational situations (human potential optimization techniques - TOP 2024), Emmanuel is also a qi gong instructor interested in the "study and reasoned use" of the I Ching as a reflexive and holistic strategic tool.
Independent expert for Collège Numérique FRANCE 2030, Emmanuel is also a National Defense Auditor (France).
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
A Zoom Virtual Event — March 23-24, 2023
With participants from over 30 countries, the 2023 BayesiaLab Spring Conference was a truly international event. If you missed some of the talks, we have now posted recordings of all presentations to our Conference Archive.
If you missed some of the talks, we uploaded recordings of all presentations and the corresponding slides.
Ibon Galparsoro Iza, Ph.D., AZTI · Marine Research Division
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
Economic activities are dependent upon natural capital (NC), which is responsible for 'Ecosystem Services' (ES). Understanding dependencies on NC provides insight into the ecosystem's capacity to maintain and develop activities in the future. To determine 'NC dependencies', we present a framework linking maritime activities (bottom trawling, artisanal fisheries, aquaculture, and tourism) to their demand for ES and, further, to the NC components responsible for their production. The framework was operationalized using a spatially explicit Bayesian Belief Network (BBN), using the Basque coast (SE Bay of Biscay) to illustrate our approach to identifying trends in the strength and spatial distribution of NC dependencies. For example, benthic trawling was dependent on sedimentary habitats, with a 'moderate' to 'high' dependency of 52% of the study area. The model can also extrapolate NC dependencies to a larger area where the activity currently does not operate, where benthic trawling was estimated to have higher utilization of ES in deeper waters. When NC dependencies are combined with economic and legislative factors, the current spatial distribution of the activity can be explained, and the potential socioeconomic impacts of management decisions could be predicted. The integrative approach contributes towards ecosystem-based spatial planning.
Ibon Galparsoro Iza, Ph.D., Principal Researcher. Marine and Coastal Environmental Management AZTI · Marine Research Division
PhD in Marine Sciences from the University of Vigo. Principal Researcher at AZTI’s Marine Research Unit. He has more than 15 years of professional experience in different lines of marine research applied to Integrated Coastal Zone Management. His main research interests include Marine Spatial Planning, assessment and mapping of marine and coastal ecosystem services, implementation of the European Marine Strategy Directive, seabed mapping and characterization, and modeling of benthic habitats.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Steven Frazier, Georgia Pacific
Presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
This presentation explores the application of machine learning and Bayesian networks to enhance tissue softness, a crucial factor in the paper product industry. It begins with the presenter's extensive background in engineering and analytics, setting the stage for a deep dive into utilizing Bayesian networks to improve tissue manufacturing processes.
The focus is on refining the balance between paper strength and softness, crucial for producing high-quality tissue. The presentation covers the technicalities of tissue production, from fiber refining to the mechanics of papermaking, illustrating the complex interplay of factors affecting product quality.
A significant portion is dedicated to the evolution of model development, from initial challenges to advanced iterations that accurately predict tissue softness. Techniques like Jackknife and K-Fold cross-validation are discussed for model evaluation, highlighting the learning curve and adjustments made to enhance model performance.
Operational insights form the core of the latter part, where data analysis reveals optimal manufacturing conditions. The presentation touches on the importance of data integrity, model adaptability, and the role of human operators in implementing AI-driven recommendations.
Concluding, the presentation reflects on the project's broader impacts, emphasizing continuous improvement, user readiness, and aligning project goals with customer expectations. This summary encapsulates the journey and lessons learned in applying advanced analytics to improve tissue softness, underscoring the potential of machine learning in industrial applications.
With over 30 years of operations experience and 3 U.S. patents, Steven Frazier is an expert in creating high-performance solutions for complex business challenges. His multi-disciplinary approach has been pivotal in enhancing processes for leading Fortune 500 companies, including Coca-Cola, Procter & Gamble, and Georgia Pacific.
Steven specializes in Bayesian Networks and machine learning, providing critical insights that guide manufacturing efficiency and strategic value creation. His innovative modeling techniques have informed smarter procurement strategies, yielding substantial cost savings and process enhancements.
During his recent role at OnPoint (Koch Industries), Steven's models for tissue softness and strength revealed significant opportunities for value creation, contributing to both product and process improvements.
As he prepares to share his expertise at the Bayesialab conference on April 11, 2024, Steven's pragmatic and transformative approach is expected to resonate with a wide audience. With a solid educational foundation in Mechanical Engineering from Washington University and advanced certification in Lean Six Sigma from Villanova University, he's geared to propel organizations towards operational excellence and analytical innovation. A presentation you don’t want to miss.
Your platform choice, whether BayesiaLab or another, doesn't restrict your participation. We welcome entries from academia, government, industry, and solution providers, focusing on new applications, ongoing work, and capabilities within Bayesian networks.
Date: April 11–12, 2024 Slot Duration: 30–45 minutes (including 5–15 minutes of Q&A)
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Accepted abstracts will be showcased on the Bayesia website before the conference. All presentations will be recorded. By submitting, you consent to your presentation and its recording being shared on the Bayesia website and related social media platforms.
All accepted presenters will enjoy a waived conference registration fee.
If you are attending a BayesiaLab Conference for the first time, please check out the archives from previous conferences.
Join us in Cincinnati and bring to life your work with Bayesian networks on our stage. The promises rich insights into the latest in applied research using probabilistic graphic models and Bayesian networks. Besides, through enlightening courses and talks, immerse yourself in best practices with the BayesiaLab software platform.
Direct your presentation proposals to .
Dr. Lionel Jouffe, Bayesia S.A.S.
Presented at the 2023 BayesiaLab Spring Conference.
Knowledge Elicitation has been a central research topic for the BayesiaLab team for many years, so the arrival of ChatGPT last year has prompted us to leverage its innovative technology immediately with BayesiaLab.
Learn More About Knowledge Elicitation
In this presentation, we will show new functions that directly integrate ChatGPT into BayesiaLab, including:
Chat Completion
Image Generation
Embedding Generation
BayesiaLab's forthcoming Subject Matter Assistant, to be released in Version 11, can improve research workflows in several ways:
Accelerate the qualitative part of knowledge elicitation.
Generate practical natural language descriptions for latent factors created through BayesiaLab's clustering functions.
Automatically create images to illustrate nodes in a network.
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
Hamza Zerrouki, Department of Process Engineering, University Amar Telidji of Laghouat
In the last few decades, chemical and process industries have become more prone to accidents due to their complexity and hazardous installations. These accidents lead to significant economic and, most importantly, human losses. Risk management is one of the appropriate tools to guarantee the safe operations of these plants. Risk analysis is an important part of risk management, and it consists of different methods such as Fault tree, Bow-tie, and Bayesian network (BN). The latter has been widely applied for risk analysis purposes due to its flexible and dynamic structure. In the current presentation, we will expose the different applications of BN in chemical and process industries with examples to explain how BN can be used to conduct a risk assessment, safety, and risk analysis of these industries.
Not available.
Hamza Zerrouki, Department of Process Engineering, University Amar Telidji of Laghouat, Laghouat, Algeria
I joined the Department of process engineering in September 2018. Before that, I was with the Institute of Occupational Health and Safety at Batna 2 University as a Ph.D. student from 2013 until 2018.
I have a bachelor's degree in industrial and environmental safety from the University of Laghouat (2011) and a master’s degree in control of industrial risks from the University of Batna 2 (2013). My research is mainly focused on the safety assessment and management of petrochemical facilities regarding technological accidents and Bayesian network applications.
Anand Wilson & Buvana Iyer, Course5 Intelligence
Presented at the 2023 BayesiaLab Spring Conference.
Identified factors that drive customer lifetime value and components that drive higher CLV to drive revenue and improve customer retention. Using data from this study, we determine the strength of the relationship between Opportunity, Lifetime Value, and Customer Ratings. Graphs, causal insights, and early warning signals are generated through network analysis. As a result, strategies are better planned, and gaps are identified and addressed more efficiently. Surveys are conducted regularly to understand how to serve our most valuable customers better. By improving customer experience (CX), we are able to increase their lifetime value (LTV). This method also enables us to make precise recommendations about how to improve Client Economics. The Bayesian implementation offers some advantages, which help in more precision and enhanced flexibility in bringing different data sets together with actionable insights.
Anand has 9+ years of experience in applied artificial intelligence and data sciences. He has worked for marque clients such as Lenovo, Intel, Microsoft, Novartis, Novo Nordisk, GE, Mars Wrigley, PepsiCo, etc., enabling digital transformation using A.I.
In his current role, Anand focuses on developing and marketing solutions based on the Bayesian Network model theory, which enables us to quantify causality in an observational study. His major areas of work/research include Knowledge Modelling, Machine Learning with BayesiaLab, and Inference.
Anand comes from an applied statistics background. He has a master's degree in statistics. Anand carries an acute interest in machine reasoning, causal inference, and experimental designs, along with machine learning and data science.
Over 15 years of international and domestic market experience with a proven track record of leading high-profile strategic projects on a fast-moving set of priorities and business initiatives to translate strategic organizational goals into clear operational plans and derive measurable results.
Buvana consults C-level clients and has led projects achieving org-level implementation of enterprise analytics, including Software Development, BI & Analytics, and ML & AI solutions (at scale), adopting DevOps philosophy with agile delivery. Expertise in leveraging both traditional statistics and machine learning techniques to create solutions and deliver business value.
Buvana comes from applied Mathematics background. She has a master's degree in Mathematics. Buvana carries an acute interest in predictive analytics, statistical modeling, machine reasoning, and experimental designs, along with machine learning and data science.
Dr. Lal Hussain, Department of Computer Science & IT, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan
Presented at the 2023 BayesiaLab Spring Conference.
Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static features to analyze a comprehensive analysis among extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. This study is focused to utilize the important highly ranked energy feature as the target node for empirical analysis of dynamic profiling and optimization to unfold the nonlinear dynamics of GLCM features extracted from Brain MRIs to distinguish the pituitary and meningioma glands.The highest strength of relationship was obtained between the nodes (Correlation→correlation2) yielding strength of relationship using KL and MI (1.4640), Pearson’s correlation (1.0000), with relative width 1.0000 and overall contribution of 16.67%. The segment profile analysis of top ranked target node with other extracted GLCM features using the Radar chart was computed which reflect the distributions based on 1 to 12 clock hours. We used the NHST t-test and Bayesian test to find the significance to distinguish with other different states such as <=0.273, c) <=0.368, d) <=0.471, e)>0.471. The clusters <=0.273, <=0.471 and >0.471 using both the test yielded the highly significant results with all the extracted GLCM features. The state <=0.368 yielded high significant results using both test with homogenity1, dissimilarity, correlation, correlation2, and autocorrelation, whereas significant results using NHST t-test with contrast and energy. Using the tornado graph, we visualize the maximum deltas in the posterior probabilities of the target states, and hard evidence is set on the selected variables. The highest association was yielded with entropy, homogeneity, dissimilarity, contrast, correlation, correlation2 cluster state <=0.273 followed by cluster state <=0.368, <=0.471, and >0.471. This indicates that high top ranked Energy feature prevails high associations with entropy, homogeneity, dissimilarity, contrast, correlation, and correlation which can be used as a better predictor for improved diagnosis and prognosis of brain tumor types. Previous studies rely on classification methods. However, this novel technique is proposed to further investigate the dynamics, associations, posterior probabilities, prior probabilities, marginal likelihood, prior means, and posterior means to further unfold the relevance and relationships among the extracted features. The proposed approach will be very helpful for improved diagnosis and prognosis of brain tumor types. The proposed method further unfolds the dynamics and detailed analysis of computed features based on GLCM features for a better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.
Dr. Lal Hussain is an Assistant Professor at the Department of Computer Science & IT, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan. He obtained his MS in Communication and Networks from Iqra University, Islamabad, Pakistan, in 2012 with a gold medal. He received Ph.D. from the Department of Computer Science & Information Technology, University of Azad Jammu and Kashmir, Muzaffarabad, Pakistan in February 2016. He worked as visiting Ph.D. researcher at Lancaster University UK for six months under HEC International Research Initiative Program and worked under the supervision of Dr. Aneta Stefanovska, Professor of Biomedical Physics, Physics Department, Lancaster University, during 2014-2015 in the UK. Dr. Hussain recently completed a one-year Postdoctoral fellowship from Montefiore Medical Center and Albert Einstein College of Medicine, New York, USA, under the supervision of Dr. Tim Q Duong, Professor and Vice Chair of MRI research. He also worked at Duong Lab at Stony Brook University USA on different ongoing projects with Dr. Duong from January-March 2020. Recently, Elsevier ranked Dr. Hussain in the top-1% of the world scientists list of 2021 based on their research record. He is the author of more than 50 publications of highly reputed peer-reviewed and Impact Fact Journals as Principal author. He completed various funded projects as PI and Co-PI from Ignite, ICT Pakistan, and the University of Jeddah, Saudi Electronic University, Kingdom of Saudi Arabia. He presented various talks in Pakistan, the UK, Peru, and the USA. His research interest includes developing and optimizing AI tools, including machine learning, deep learning, and neural network algorithms, feature extraction and selection methods, information-theoretic methods, time-frequency representation methods, and cross-frequency coupling to predict disease severity, progression, survival, and recurrence. His area of interest includes biomedical signal and image processing problems, including prostate cancer, breast cancer, lung cancer, brain tumor, covid-19 lung infection with different modalities (i.e., MRI, CT, X-Ray, etc.), brain dynamics and diseases (i.e., autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), Alzheimer's Disease).
Andrew Holle & Becky Gibbs-Murray, American Innovations
Presented at the 2023 BayesiaLab Spring Conference.
In this presentation, we explore how BayesiaLab can work to identify trends in the DOT’s Pipeline and Hazardous Materials Safety Administration’s public incident and annual report data to identify strategies that pipeline operators can leverage to reduce incidents and predict failures more effectively.
Andrew Holle has been with American Innovations for over nine years and currently serves as the Senior Principal Systems Architect. Andrew has a BS in Chemical Engineering and an MS in Computer Science. He is currently focusing on innovative ways to bring the benefits of machine learning to customers through our product offerings.
Becky Gibbs-Murray has focused her entire career on building innovative, user-friendly tools that advance the oil and gas industry. She joined American Innovations in October 2021 as Senior Product Manager and holds a degree in Chemical and Petroleum Engineering from the Colorado School of Mines.
Ahsana Parammal Vatteri, Chair on Disaster Risk Reduction and Resilient Engineering
Presented at the 2023 BayesiaLab Spring Conference.
Natural hazards such as floods and earthquakes pose a significant risk to the lives and livelihood of people living in exposed regions and affect the continuation of education of students, which is a fundamental right. Yearly flood events with low inundation depths may not cause structural damage, however, resulting material degradation contributes to higher vulnerability to subsequent seismic events. This combined effect of flood and seismic hazards, along with other functional losses, ultimately results in disruption to education delivery. The socio-economic condition of the users-community also plays a role in the extent of such disruption. This problem demands consideration of a large number of dimensions to estimate the impact on the school system infrastructure in a locality. A Bayesian network (BN) model is proposed to handle the qualitative and quantitative nature of these variables, representing multiple schools in a locality as a system. Three factors are considered to contribute to the system disruption, namely, schools’ physical functionality loss from damage to infrastructure, accessibility and change of use, and social vulnerability. The impact is quantified through the probability of the system being in various states of disruption, which can support decision-making and strategic planning in the face of multiple hazards.
Ahsana Parammal Vatteri is a research fellow on the UNESCO Chair on Disaster Risk Reduction and Resilient Engineering, chaired by Prof. Dina D’Ayala at UCL. She works in the EPICentre research group in the field of earthquake and multi-hazard engineering and the application of Bayesian Networks for system resilience problems. She focuses on the confined masonry typology of buildings, especially school buildings, and the resilience of education infrastructure. Her other engagements include consultancy projects on seismic-safety assessment of schools for DFID UK, the 2020 EEFIT Aegean Earthquake and Tsunami reconnaissance mission, modified taxonomy for confined masonry school buildings for GPSS project for the World Bank, SECED Young Members Subcommittee, etc.
Xingang Zhao, Ph.D., Oak Ridge National Laboratory
Presented at the 2023 BayesiaLab Spring Conference.
Future advances in nuclear power technologies call for enhanced operator advice and autonomous control capabilities. One of the first tasks in developing such capabilities is the formulation of symptom-based conditional failure probabilities for power plant structures, systems, and components (SSCs). The primary goal is to aid plant personnel in (1) deducing the probabilistic performance status of the monitored SSCs and (2) detecting impending faults/failures. The task of estimating conditional failure probability is a bidirectional inference problem, and a logical approach is to use the Bayesian network (BN) method. As a knowledge-based artificial intelligence tool and a probabilistic graphical model, BN offers reasoning capability under uncertainty, graphical representation emulating the physical behavior of the target SSC, and explainability of the model structure and results. This presentation will provide an overview of the BN technique and the software tools for implementing BN models in this task, along with the associated knowledge representation and reasoning paradigm. The challenges with data availability and the general approach to target SSC identification will be highlighted. Two example case studies on the failure of (1) a centrifugal pump and (2) an electric motor will be presented to demonstrate the usefulness and technical feasibility of the proposed BN-based fault diagnostic artificial reasoning system using expert system shells.
Dr. Xingang Zhao is an R&D Scientist at Oak Ridge National Laboratory. He received his Ph.D. in nuclear science and engineering from the Massachusetts Institute of Technology. His research interests span multiple disciplines of clean energy systems and their intersections with artificial intelligence and decision science. He has been a major contributor to a diverse portfolio of research projects that advance the state of the art of modeling & simulation and digital engineering for nuclear and renewable energy applications.
María Pazo Rodríguez, School of Mining and Energy Engineering, University of Vigo
Presented at the 2023 BayesiaLab Spring Conference.
Heavy economic activities such are industry and agriculture strongly limit soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment. In this respect, it is critical to identify areas that require remediation. In the herein research, a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al, and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Next, a stratified systematic sampling method was used at short, medium, and long distances from each area to obtain representative visualization of the total variability of the chosen attributes. The information was then combined into four risk classes (low, average, high, and in need of remediation) based on multiple sediment quality guideline (SRM) baseline values. Bayesian analysis, inferred for each area, was used to characterize PET correlations, with the unsupervised learning network technique being the best solution. According to the Bayesian network structure obtained, Pb, As, and Mn were chosen as key contamination parameters. For these three elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Lastly, BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The mean image and standard deviation maps were obtained, resulting in high/low-risk clusters (local G clusters) and spatial uncertainty calculation. High-risk clusters are mostly located in the area with the highest elevation (agriculture/livestock) associated with low spatial uncertainty, which indicates the need for remediation. Air emissions, primarily from the metal industry, contribute to soil contamination by ETPs.
María Pazo Rodríguez is a doctoral student at the School of Mining and Energy Engineering affiliated with the University of Vigo.
My research is focused on developing Bayesian models that can guide the implementation of digital transformation in the mining and energy sector. To this end, the main objective is to equip the mining industry with risk assessment tools and decentralized decision models that enable its rapid decarbonization, thus contributing to meeting the growing demand for strategic minerals and addressing the strict environmental policies imposed by national and international organizations.
Minghui Wang, Ph.D., Icahn School of Medicine at Mount Sinai
Presented at the 2023 BayesiaLab Spring Conference.
Understanding the molecular regulatory networks underlying insulin resistance (IR) is crucial to preventing type 2 diabetes and related metabolic disorders. However, our knowledge of the landscape of IR-related transcriptomic regulation in glucose-responsive tissues and its cell-type specificity regulatory mechanisms remains incomplete. To provide a comprehensive population-level understanding of the organizations and cell-type-specific regulations of gene expressions underlying IR,
we employed an integrative network biology approach to integrate the multi-omics and phenotypic data from well-powered African American (AA) and European ancestry (EA) cohorts. By integrating the state-of-art single-cell sequencing data analyses with bulk-tissue expression quantitative trait loci (eats), and coexpression and Bayesian causal networks, we presented trans-ethnic and cross-tissue results of IR in adipose and muscle tissues. We identified ethnically conserved cell-type signatures and gene modules associated with insulin sensitivity responses. We further prioritized modules enriched for cis-eQTL genes and predicted network driver genes for experimental validations. Together, this study revealed the cell-type-specific transcriptomic networks and signaling maps underlying insulin resistance in major glucose-responsive tissues.
Dr. Minghui Wang is an Associate Professor in the Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai. Dr. Wang was trained in statistical and quantitative genetics during his graduate studies at Fudan University and postdoctoral research at the University of Birmingham. At Icahn School of Medicine at Mount Sinai, his research focused on developing novel integrative network models that combine multi-layers of bulk-tissue and/or single-cell functional genomics data to uncover the cellular changes and hidden regulatory relationships among a large network of genes/proteins in disease-relevant tissues for human aging and human complex disorders like Alzheimer’s disease (AD), cancers, and diabetes, etc.
Mahmudur Fatmi, Ph.D., University of British Columbia – Okanagan
Agent-based microsimulation modeling techniques are adopted for urban system modeling mainly because of their capacity to address the complex interactions among individuals, households, and other urban elements. The performance of urban simulation models largely depends on the quality of the input data, which is generated through a population synthesis procedure. This study proposes a Bayesian network and generalized raking techniques for population synthesis. The Bayesian network is used to generate the synthetic population pool from the microsample, and generalized raking is used to fit the synthetic population with the control total. Some of the key features of the proposed population synthesis are as follows: accommodating heterogeneity based on both household and individual attributes, tackling missing/incomplete observations in the microsample, and generating a true synthesis of the population from the microsamples. A data-driven structure learning technique is adopted to generate effective and optimal structures among heterogeneous households and individuals. This Bayesian network + generalized raking procedure is implemented to generate a 100% synthetic population at the smallest zonal level of dissemination area for the Central Okanagan region of British Columbia. The results suggest that capturing heterogeneity within the Bayesian network has tremendously benefitted the reconstruction process in efficiently and accurately generating a synthetic population from the available microsample. Finally, this population synthesis is developed as a component of the agent-based integrated urban model, currently under development at The University of British Columbia’s Okanagan campus.
Dr. Mahmudur Fatmi is an assistant professor in Civil Engineering at UBC-Okanagan. His expertise revolves around travel demand modeling, particularly focusing on the interactions among population socio-demographics and their transportation and land use-related decisions. He contributes to developing econometric models and agent-based microsimulation techniques. He has partnerships with cities and transit agencies, and his research findings assist them in making effective transportation and land use policies and infrastructure investment decisions. He is a member of several transportation research and professional communities and societies. Dr. Fatmi’s contributions have been recognized through several local, provincial, and national awards, such as Transport Canada Scholarship and Nova Scotia Research and Innovation Scholarship.
Presented at the .
Steven F. Wilson, Ph.D., EcoLogic Research
Presented at the 10th Annual BayesiaLab Conference on Monday, October 24, 2022.
Abstract Significant environmental degradation is rarely the result of a single, acute event but is more often caused by the additive and/or synergistic effects of several stressors. Assessing these “cumulative impacts” is an important component of environmental assessments, but the procedures for calculating such impacts are theoretically weak and could generate misleading estimates of project impacts. Here, I propose a framework for analyzing cumulative environmental impacts that is rooted in causal theory. Specifically, I argue for the application of causal models and the explicit incorporation of “rung three” counterfactual reasoning from Pearl’s causal hierarchy. The important but underused concepts of necessary and sufficient causation are prominent in the proposed framework and lead to surprising assessment results when used to estimate the effects of industrial development on grizzly bear and caribou populations.
Steven F. Wilson, Ph.D., EcoLogic Research, 302-99 Chapel Street, Nanaimo, BC V9R 5H3, Canada, steven.wilson@ecologicresearch.ca
Steve Wilson has 30 years of experience working at technical and professional levels in strategic and operational planning for wildlife and other ecological values. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.
The Social Graph—Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada (Paris, 2017)
Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management (Nashville, 2016)
Big data, small data: Bayesian networks in environmental policy analysis in Canada’s energy sector (Fairfax, 2015)
Vuong Pham, Ph.D., CMCC@Ca’Foscari
Extreme weather and climate-related events, from river flooding to droughts and tropical cyclones, are likely to become both more severe and more frequent in the coming decades, and the damages caused by these events will be felt across all sectors of society. In the face of this threat, policy- and decision-makers are increasingly calling for new approaches and tools to support risk management and climate adaptation pathways that can capture the full extent of the impacts. In this context, Bayesian Network (BN) stands as a novel and powerful approach for the capturing and modelling of multi-risk against future ‘what-if’ scenarios.
Building on a risk-based conceptual framework, several BN models were developed, trained, and validated by expert judgment and database-driven to support multi-risk scenario analysis with various aims such as multi-sectoral flooding damages, marine cumulative impacts, ecosystem services assessment in different domains (e.g., freshwater, marine, and coastal, agriculture and industry). A major advantage across these applications lies in the possibility of combining heterogeneous data from multiple sources and across different domains, which is vital in environmental risk assessment.
The outcome of these applications represents valuable support for disaster risk management and reduction actions against climate change and extreme events, enabling better-informed decision-making. Furthermore, more ambitious development could involve the spatialization of the output of the model with a user-friendly interface, building on the GIS-based structure of the training dataset, to assist policy- and decision-makers in using the results of these applications to prioritize more efficiently plans for Disaster Risk Management and Climate Change Adaptation.
Vuong Pham holds a Ph.D. in Science and Management of Climate Change from Ca’Foscari University (Italy) and an MSc. in Environmental and Geomatic Engineering from Politecnico di Milano (Italy). He has been affiliated with the Euro-Mediterranean Center on Climate Change since 2016, collaborating in the research activities within the projects of the Risk Assessment and Adaptation Strategies Division at CMCC@Ca’Foscari (Venice).
Vuong’s research focuses on multi-risk assessment, including the issues related to freshwater, coastal areas, and ecosystem services capacity associated with these domains. He and his research developed several BNs applications to support multi-risk scenario analysis with various aims such as multi-sectoral flooding damages, marine cumulative impacts, and ecosystem services assessment.
Presented at the on Monday, October 24, 2022.
Dr. Lionel Jouffe, Bayesia S.A.S.
Presented at the 10th Annual BayesiaLab Conference on October 24, 2022.
Knowledge elicitation from domain experts is a key area of interest for the BayesiaLab Team. In this year's technology presentation, Dr. Lionel Jouffe introduces several innovations in structural knowledge elicitation, which are now implemented in the BEKEE (Bayesia Expert Knowledge Elicitation Environment) workflow.
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
Dr. Renan Rocha & Dr. Francisco de Assis de Souza Filho, Federal University of Ceará
Presented at the 10th Annual BayesiaLab Conference on Monday, October 24, 2022.
The thesis entitled “Bayesian Networks and Network Science Applied to Water Resources: Streamflow Analysis and Forecast incorporating the Non-Stationarity” aimed to develop methodologies to (1) identify the existence of changes in the streamflow time series and its location, (2) incorporate this aspect in the streamflow modeling and forecasting framework and (3) analyze the full extension regarding its impact. The focus on Bayesian Networks, as an alternative approach to classical streamflow modeling methodologies, relied upon recent articles that indicated Bayesian Networks as a promising tool in hydroclimate studies, simultaneously providing good modeling results and allowing causal discovery through the analysis of the network structure. A first attempt to incorporate this non-stationarity was made using Gaussian Bayesian Networks (GBN). Discrete variables representing the different phases of low-frequency oscillations were included in the networks, allowing different network parameters according to the phases. The results demonstrated the great potential of the GBN to forecast streamflow with lead times from one to eight months. The results also unveiled a good streamflow forecasting potential via Bayesian Inference based on Likelihood Weighting simulations. The use of the phases resulted in performance improvement for some stations, however, it did not improve the results of the stations that presented changes in the time series, suggesting significant changes between the network structures of each homogeneous period. Network structures were obtained through different methodologies for each homogeneous period to analyze this aspect. The results confirmed the initial hypothesis, showing significant differences between the network structures of each homogeneous period, with alterations in the relationship between the variables and their autocorrelation function. Therefore, the use of the same set of parents for the complete series may not comprise the extension of the changes observed.
Dr. Renan Rocha is a Civil Engineer with a Master's and Doctor degree in Water Resources from the Federal University of Ceará (UFC). Currently works as a Researcher in FUNCEME, Institute for Research in Meteorology, Water Resources, and Environment, and is the Head of the Water Resources Department (GEPEH). Has experience with Time Series Analysis, Hydrological Modelling, Bayesian Networks, Complex Networks, Drought analysis, and NEXUS (Water-Energy-Food). His recent thesis explored the use of Bayesian Networks to forecast streamflow incorporating the Non-Stationarity existence.
Dr. Francisco de Assis de Souza Filho is a professor at the Hydraulics and Environmental Engineering Department of the Federal University of Ceará and the Head Scientist of Water Resources at the Ceará State Foundation for the Support of Scientific and Technological Development. Has a Doctor degree from São Paulo University and a postdoctoral internship at the International Research Institute for Climate and Society of Columbia University. Has won several awards, including the Engineer Francisco Gonçalves Aguiar Medal, the highest commendation of the Water Resources of Ceará. Was the head of water resources-related organizations, such as FUNCEME and ABRH.
Edwin Hui, University of St Andrews, Scotland
Presented at the 10th Annual BayesiaLab Conference on Monday, October 24, 2022.
Understanding the dynamics that regulate ecological resilience is becoming increasingly important in today’s world, as ecosystems are facing multiple pressures on global, regional, and local scales. If pressures exceed a threshold, this may trigger a regime shift, where a system undergoes a step change to another state that can last for substantial periods of time. Recent applications of Bayesian networks (BNs) have shown promise in revealing network structures of complex systems, and such understanding shows great promise for the understanding of mechanisms underlying the resilience of complex systems. In this talk, we present two case studies to document the potential of Bayesian Networks in the study of complex systems:
In recent years, the use of Bayesian networks (BN) has seen successful applications in molecular biology and ecology, where it was able to recover known links in the respective systems it was applied to. While this is invaluable in ecology, an unexplored application of BNs would be utilizing it as a novel variable selection tool in the training of predictive models. To this end, we evaluate the potential usefulness of BNs in two aspects: (1) we apply BN inference on species abundance data from a rocky shore ecosystem, a system with well-documented links, to test the ecological validity of the revealed network; and (2) we evaluate BNs as a novel variable selection method to guide the training of an artificial neural network (ANN).
To date, two distinct approaches have emerged in the study of ecological resilience. On one hand, network-based approaches have successfully revealed ecological network structures of complex systems. On the other hand, novel non-additive modelling frameworks have been developed and allowed for the direct quantification of ecological resilience. So far, these two approaches have been largely segregated. However, connecting these two fields may offer novel insight into the study of ecological resilience. Here, we propose a novel 2-step modelling process to study ecological resilience and regime shifts: (1) we apply the Integrated Resilience Assessment (IRA) framework proposed by Vasilakopoulos et al. (2017) to quantify and approximate the ecological resilience of ecosystems under study; and (2) we apply a dynamic Bayesian Gaussian Mixture (BGMD) Bayesian network model to reveal the network structure with a changepoint process to take the temporal structure into account.
Edwin Hui is a Ph.D. student from the University of St Andrews, where his research focuses on developing computational models to study resilience and regime shifts across complex systems. He is interested in applying a variety of statistical and computational tools to address ecological questions and study complex systems theory. Throughout his Ph.D., he aims to develop novel computational approaches to study complex systems across disciplines, ranging from ecological to macroeconomic systems.
Olivier Cussenot, MD, Ph.D., Sorbonne University
Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.
Comprehensive tools to drive decision-making are new challenges for the revolution of precision and preventive medicine. These tools could optimize management or assess better the risk of having or developing diseases and help practitioners make decisions to perform deep (invasive or costly) diagnosis procedures. So, Bayesian statistical methods and modeling techniques provide a powerful approach to integrate knowledge and new markers to refine the probability of outcome and decision-making in clinical practice. Bayesian networks through BayesiaLab offer the opportunity to search for better, more informative factors or a combination of these factors to enhance endpoint prediction. For example, we present some applications for risk prediction and personalized management in prostate cancer. In this way, Bayesian approaches, using BayesiaLab, have been shown a powerful strategy to explore, validate, and translate useful multifactorial predictors for precision medicine to clinical practice.
Professor Olivier Cussenot, MD, Ph.D., is a full professor at Sorbonne University. He is qualified as a urological surgeon, oncologist, and geneticist and has a minimum of two decades of experience in molecular/translational prostate and urological cancer research.
As head of the department, he managed a research unit on predictive oncology and personalized prevention strategies. He was also the principal investigator of many national and European research programs on urological and prostate cancers. His research programs are mainly on the clinicopathological and molecular heterogeneity of cancers related to the germline genetic background (family history or ancestry) and the interaction with the environment. He led the French Institute of Cancer (INCa) and the French Cancer Research League, the national programs on prostate cancer genomics (French part of the ICGC and molecular tumor ID). He also led the first national program, which links genetic markers to the national health database to model the different life pathways according to the different prostate cancer management, co-morbidities, and individual genetic or mesological factors.
He is the author of more than 480 scientific articles referenced as “Cussenot O.” They are available on the PubMed data search engine. He wrote more than 60 didactic chapters in books and provides translational seminars on genomics and artificial intelligence /decision-making in urologic oncology.
Hussein Jouni, L’Oréal Research & Innovation
Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022, at 13:30 (UTC).
Capitalizing on expert knowledge can be useful for a company. It can be for transmitting all the know-how on a given field, incorporating technical aspects for decision making, or building causal models for doing prediction. This knowledge can be represented through a Bayesian Network in order to introduce uncertainty on the phenomenon, and, combined with Data, its performance can be improved. Elicitation is done thanks to sessions where expert works together to build models thanks to a facilitator and a modeler. The experts are asked to be available for a given amount of time, which can be large (several days), with a risk that at the end of the sessions, they will not be able to have a satisfying tool. In the context of multi-project management, we propose a tool to assess the probability of success of Elicitation sessions on a given problem. This tool is obtained thanks to the Elicitation of a Bayesian Network (meta-model).
Hussein Jouni, Statistical Engineer at L’Oréal Research & Innovation
I studied biomedical engineering at ESIEE PARIS and at the Faculty of Medicine of Paris XII University. After obtaining my degree and my first experience at Danone Nutricia Research, I specialized in clinical data science and clinical research. I’ve been working for L’Oréal (Research and Innovation division) for five years as a Statistical Engineer – Data Scientist.
Mikael Rubin, Ph.D., Palo Alto University
Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 25, 2022.
Depression is a highly heterogeneous mental health concern, making it difficult to determine optimal treatment approaches. Mindfulness-based interventions have been shown in meta-analyses to be moderately effective in reducing symptoms of depression. Research identifying mechanisms governing the efficacy of mindfulness-based interventions might be guided by analytic approaches that can generate hypotheses to test in future research. Network analysis (often utilizing Gaussian Graphical Models) is an item-level approach widely used in recent psychological research to understand interrelations within and between heterogeneous mental health constructs.
To identify the interrelations between symptoms of depression and features of mindfulness, we used Bayesian Network Analysis across three cross-sectional samples (N = 1,135). Bayesian Gaussian Graphical Models allowed us to (1) generate an exploratory network in two samples using different depression assessments: the Patient Health Questionnaire (n = 384) and the Depression Anxiety and Stress Scale (n = 350), with mindfulness being assessed using the Five-Facet Mindfulness Scale and (2) confirm findings from the exploratory network in a third sample (n = 401) with a pre-registered replication.
From the exploratory analyses, we found that the Non-judging facet of mindfulness (reflecting acceptance of thoughts and feelings) was the most central (i.e., interconnected) bridge to symptoms of depression. The pre-registered analysis confirmed our initial findings: after controlling for all other associations, Non-judging represented the most central connection between facets of mindfulness and depression. These results suggest that when considering the use of mindfulness-based interventions for individuals with depression, examination of Non-judging is warranted and may offer a potent target.
Mikael Rubin is an Assistant Professor at Palo Alto University. He received his Ph.D. in clinical psychology from the University of Texas at Austin. From studying virtual reality in art to conducting virtual reality exposure therapy, he is curious about how what we attend to influences how we make meaning out of the lived experience. He specializes in research and interventions related to anxiety and post-traumatic stress. His research has used a wide range of approaches (including eye tracking, neuroimaging, and network analysis). He directs the Transdiagnostic Attention Intervention (TRAIN) Lab at Palo Alto University and is especially interested in using virtual reality and eye-tracking methods to evaluate, enhance, and widely disseminate mental health interventions.
John Carriger, Ph.D., U.S. Environmental Protection Agency
Presented at the 10th Annual BayesiaLab Conference on Monday, October 24, 2022.
Environmental assessments require endpoints representative of ecological communities. These can include summary indicators or indicators for different components of the community. However, summary indicators applied to a complex system can sometimes create mathematical challenges that result in metrics that are ambiguous or uninterpretable. Coral reefs are complex ecosystems, so patterns of ecological interactions were explored by probabilistic clustering of reef monitoring variables with Bayesian networks. In 2010 and 2011, the U.S. Environmental Protection Agency sampled coral reef communities along the coast of Puerto Rico with probabilistic surveys, and the data were examined in a clustering analysis with Bayesian networks. Most of the component variables (gorgonians, sponges, fish, and coral) were found to have stronger associations within than between taxa, but unsupervised structure learning with lowered complexity weights identified two cross-taxa relationships. Survey data were also used in data clustering analyses to identify site clusters for sponge, gorgonian, stony coral, and fish variables. These clusters were constructed using an expectation-maximization algorithm that created a factor node jointly characterizing the density, size, and diversity of individuals in each taxon. The clusters were interpreted in terms of their relationship with the monitoring variables used in their construction and the relationship of the fish clusters to the monitoring variables for other taxa, such as stony coral variables. Each of these factor nodes was then used to create a set of meta-factor clusters that further summarized the aggregate monitoring variables for the four taxa. Once identified, taxon-specific and meta-clusters can be applied on a regional or site-specific basis to better understand reef communities in terms of ecosystem services and risk assessment.
EPA Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
John F. Carriger U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Cincinnati, Ohio, USA
William S. Fisher U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Measurement and Modeling, Gulf Breeze, Florida, USA
John Carriger is a research scientist at the U.S. Environmental Protection Agency’s Office of Research and Development in Cincinnati, Ohio. John has a marine science Ph.D. from the College of William and Mary. John’s research interests include applying risk assessment, decision analysis, and weight of evidence tools to environmental problems.
Ali Fahmi, Ph.D., University of Manchester
Bayesian networks (BNs) have been widely proposed for medical decision support, perhaps because they can be built from knowledge and data. In my Ph.D., we examined how BNs can be used for decision-support challenges of chronic diseases, and we focused on Rheumatoid Arthritis (RA), as a case study. Three stages of this decision-support included diagnosis, self-management, and personalised care, with progressively less available data. For diagnosis, various criteria are proposed by clinicians for early diagnosis of RA, but these criteria are deterministic and cannot deal with diagnostic uncertainty. We built a BN model for diagnosing RA using an available dataset and experts’ knowledge. We obtained promising results (AUROC=0.84), and we compared them with those of an alternative BN model entirely learned from data (AUROC=0.71). We argued that a clinically meaningful structure of a BN model allows us to explain clinical scenarios in a way that cannot be done with the model learned entirely from data. For self-management, we intended to estimate disease activity remotely and frequently (e.g., weekly), instead of the current clinical practice of disease activity measurement once in 3 to 6 months, when an urgent visit and medication review may not be needed. We built two dynamic BN (DBN) models using experts’ knowledge and a set of manipulated data to predict appointment scheduling and medication review. Both models indicated acceptable performance; AUROC of the first DBN was 0.69, and AUROC of the second DBN was 0.66. The third stage of decision-support focused on personalised care for living with RA since it can have a profound impact on quality of life (QoL). We used experts’ knowledge and literature to build a BN that predicts QoL and helps to personalise the recommendations for enhancing QoL. The obtained recommendations for a set of scenarios were comparable with those of the experts.
Presentation Video
Ali Fahmi is a post-doctoral researcher in statistics and epidemiology at the University of Manchester, United Kingdom. He holds a Ph.D. in computer science from Queen Mary University of London, an MSc in management engineering from Istanbul Technical University, Turkey, and a BSc in industrial engineering from the University of Tabriz, Iran. His Ph.D. research focused on creating decision support systems with causal Bayesian network models for diagnosis, self-management, and personalised care. Currently, he is doing research in the framework of the BRIT2 project, aiming to develop and evaluate a knowledge-support for prescribing antibiotics for common infections in primary care. This project also evaluates the indirect effect of the Covid19 pandemic on prescribing antibiotics for common infections. His main research interests are decision support systems and their applications in medicine and healthcare. His main extracurricular activity is designing carpet patterns and weaving carpets.
Presented at the on Tuesday, October 25, 2022.
Chetan S. Kulkarni, Ph.D., KBR Inc., NASA Ames Research Center
Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.
Aeronautics research is at the forefront with the advent of electric vertical takeoff and landing (eVOTL) vehicles as a mode of transport. These vehicles are expected to travel in low-altitude airspace, from small drones for package delivery to larger, on-demand, urban air mobility (UAM) vehicles. The foreseeable high traffic density suggests that many of these electric propulsion systems will enter the airspace and that they will also operate at high frequency. The reliability of such critical systems is, therefore, key to ensuring high safety standards in low-altitude airspace, especially when moving in dense urban environments. Diagnostic systems, which aim at identifying incipient faults, can mitigate unexpected failures by performing early fault detection in critical systems through monitoring. The proposed approach leverages a combination of failure mode and effect analysis (FMEA) integrated with Bayesian networks, thus introducing dependability structures into a diagnostic framework.
Faults and failure events from the FMEA are mapped within a Bayesian network, where network edges replicate the links embedded within FMEAs. A key element of fault diagnosis is fault detection and isolation (FDI), which increases in complexity with the complexity of the system itself, namely the number of subsystems and components, interactions among sub-systems, and sensor availability. The developed framework enables the fault isolation process by identifying the probability of occurrence of specific faults or root causes given evidence observed through sensor data. This is demonstrated through a case study applied to the electric powertrain system of a small, rotary-wing unmanned aerial vehicle (UAV). The proposed work integrates the early design phase of an electric propulsion system with diagnostic tools, often developed later in the product lifecycle. Failure mode and effect analysis (FMEA) derived for the system in the design phase is embedded within a Bayesian network (BN).
Chetan S. Kulkarni is a staff researcher at the Prognostics Center of Excellence and the Diagnostics and Prognostics Group in the Intelligent Systems Division at NASA Ames Research Center. His current research interests are in Systems Diagnostics, Prognostics, and Health Management. Specifically focused on developing physics-based models, prognostics of electronic systems, energy systems, exploration ground systems, and hybrid systems.
He completed his MS ('09) and Ph.D. ('13) from Vanderbilt University, TN where he was a Graduate Research Assistant with the Institute for Software Integrated Systems and the Department of Electrical Engineering and Computer Science. He completed his BE (02) from the University of Pune, India. Before joining Vanderbilt, he was a Research Fellow at the Department of Electrical Engineering, IIT-Bombay, where his research work focused on developing low-cost substation automation system monitoring and control devices and partial discharge of high voltage transformers. Earlier he was a member of the technical team of the Power Automation group at Honeywell, India, where he was involved in turnkey power automation projects and product development in substation automation.
He is KBR Technical Fellow and AIAA Associate Fellow. Associate Editor for IEEE, SAE, and IJPHM Journals on topics related to Prognostics and Systems Health Management. He has been the Technical Program Committee co-chair at PHME18, PHM20-22. And co-chairs the Professional Development and Education Outreach subcommittee in the AIAA Intelligent Systems Technical Committee.
Alexandra Chirilov, James Pitcher, and Andrzej Surma, GfK
Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 26, 2022.
The topic of the presentation is comparing the different ways of calculating the feature importance: Shapley Value Regression in R, Bayesian Network in BayesiaLab software, Random Forest in R and shap library in Python. The paper aims to show the similarities and differences between considered approaches.
The entire process is based on the data simulation using copulas in which different scenarios are tested to account for the limitations of survey data, e.g., data skewness.
In the paper, the author tests the different strengths of relationships between the independent variables, the number of predictors, and the measurement scales (binary, Likert scale).
The author used a model agnostic approach called permutation feature importance as a comparison benchmark.
Please also see the second presentation by GfK at the BayesiaLab Conference:
Alexandra Chirilov is leading GfK’s Global Product Development Practice for consumer and brand intelligence. Her insights and research have been featured in publications such as Esomar, Journal of Marketing Research, Sawtooth, and more. She is a winner of the ESOMAR Corporate Young Professional Award, among other industry awards.
James Pitcher leads GfK’s Marketing Sciences team in the UK, which designs and delivers sophisticated analytical solutions to solve client high-value problems. He has spent the last 15 years providing statistical advice and consultancy within the market research industry, working with clients across many different sectors and regions. James is an expert in conjoint analysis, brand research, pricing, consumer segmentation, and a wide range of multivariate techniques, including Bayesian Networks analysis, contributing to the development of innovative techniques and regularly presenting at international conferences.
Andrzej Surma works as a methodological lead for the Global Product Development Team. He has more than ten years of experience in data analysis. With a background in mathematics, Andrzej loves to solve problems, such as recognizing the Greek letters contained within formulas that describe mathematical models! Recently, he co-created a Bayesian Networks approach to running Key Drivers Analysis on brand tracking data. Spatial data analysis is another particular interest of his. Andrzej likes to be active in his free time, playing football and riding bikes and is inspired daily by his wife and their three children.
Dave Barry & John Krech, Optum
Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 26, 2022.
Do you know where the biggest return on investment will be when designing customer experience improvements? Where in the Customer Journey do you focus? BayesiaLab helps connect disparate data sets to find nodes with the highest probability of raising satisfaction if improvements are made.
Dave Barry is a Director and team leader at Optum in the Enterprise Reporting & Analytics department. Part of UnitedHealth Group, his team at Optum aims to support UHG’s mission of helping the health system work better for everyone. Developing an approach to analyzing customer feedback, that method is being used to develop roadmaps that lead towards helping improve the Consumer, Provider, and Client’s Customer Experience (CX). Past roles have included global leadership roles in IT operations, application development, and program management at General Electric. Dave has a degree in Management Information Systems and is a certified Master Black Belt in Six Sigma Quality.
John Krech is a Principal Data Scientist at Optum, a part of UnitedHealth Group. John has undergraduate degrees in Chemical Engineering, Materials Science & Engineering, and a Master of Business Administration. John’s current focus is developing an approach to find leading indicators of future customer satisfaction. The end goal is to help improve the Customer Experience for Consumers, Providers, and Clients. Prior to Optum, John held research and development roles in Supply Chain, Manufacturing, and New Business Development at 3M. John is also a certified Master Black Belt in Six Sigma Quality.
Presented at the 10th Annual BayesiaLab Conference on Thursday, October 27, 2022.
Nuclear data evaluation is concerned with the collection and joint uncertainty quantification of data from nuclear physics experiments with the goal of producing precise estimates of nuclear quantities that can be used in applications ranging from astrophysics over nuclear medicine to nuclear energy. Data stemming from different experiments are often not directly comparable due to experimental aspects, such as the finite energy resolution of detectors, but are nevertheless related to each other as they link back to the same fundamental nuclear quantities. The Bayesian network framework is particularly well suited to model these relationships and bears the promise to accelerate the production of high-quality nuclear data evaluations in the future and to facilitate the consideration of physical constraints that are often not explicitly modeled.
Dr. Georg Schnabel works in the Nuclear Data Section in the Division of Physical and Chemical Sciences of the International Atomic Energy Agency. His responsibilities include the development of scientific codes for data analysis and management and the organization and coordination of technical meetings and workshops on topics ranging from nuclear data libraries to machine learning with the objective of improving the availability, comprehensiveness, and quality of nuclear data. Prior to working at the IAEA and after graduating from the Technical University of Vienna in Austria, Dr. Schnabel was working as a researcher at the University of Uppsala in Sweden and the French Alternative Energies and Atomic Energy Commission (CEA) specializing in the development and application of Bayesian methods for uncertainty quantification in the domain of nuclear physics.
Anand Wilson & Chiranjiv Roy, Ph.D., Course5 Intelligence
Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 26, 2022.
Surveys of primary search are conducted at different frequencies across segments to determine satisfaction metrics and brand perception for the brand and its competitors. A telemetry of the consumer is also measured by the number of issues, severity, the time required to resolve, and satisfaction by category. Modeling begins with exploratory factor analysis and probabilistic structural equations. The results show that these factors affect consumers' brand trust and are visualized in a network graph of association. The relationship network structure is derived using both controllable and latent factors, including service history and surveys, as well as socioeconomic and market factors.
As part of my talk, I will elaborate on the importance of brand trust in commercial consumer behavior and marketing management, particularly for cloud services. The purpose of this research is to investigate the effects of factors on consumers’ brand loyalty in products and service businesses leading to trust and love and recommend constraint-driven changes to improve.
Anand has 10+ years of experience in applied artificial intelligence and data sciences. He has worked for marque clients such as Lenovo, Intel, Microsoft, Novartis, Novo Nordisk, GE, Mars Wrigley, PepsiCo, etc., enabling digital transformation using A.I.
In his current role, Anand focuses on developing and marketing solutions based on the Bayesian Network model theory, enabling us to quantify causality in an observational study. A major area of work/research includes Knowledge Modelling, Machine Learning with BayesiaLab, and Inference.
Anand has a master's in statistics and a background in applied statistics. He is acutely interested in machine reasoning, causal inference, experimental designs, machine learning, and data science.
Chiranjiv has spent 20+ years across the analytics industry along with a Ph.D. in Applied Data Sciences in incubating, leading, and driving Data Analytics, Engineering, Science & AI Product Development across organizations such as Nissan Motors, Mercedes-Benz, Hewlett Packard, and HSBC Data Analytics. Chiranjiv has filed patents and developed products and solutions by applying applied AI with data for connected mobility, shared SaaS, autonomous/smart systems, AI-IoT, and electric. He is an official member of the Forbes Technology Council and the International Group of Artificial Intelligence and contributes to solving global clientele problems around Industry 4.0, Digital Manufacturing, Mobility, Advanced Analytics, Applied AI, Operations Research, and Sustainability.
Presented at the 10th Annual BayesiaLab Conference on Thursday, October 27, 2022.
Explanation in Artificial Intelligence is often focused on providing reasons for why a model under consideration and its outcome are correct. Recently, research in explainable machine learning has initiated a shift in focus on including so-called counterfactual explanations. In this presentation, we present our recent proposal to combine both types of explanation in the context of explaining Bayesian networks. To this end, we introduce “persuasive contrastive explanations” that aim to provide an answer to the question “Why outcome X instead of Y?” posed by a user. In addition, we discuss an algorithm for computing persuasive contrastive explanations and suggest how these explanations could be used in an interactive session with the user.
Silja Renooij (Utrecht University) is a member of the Intelligent Systems group and is interested in Probabilistic Graphical Models. Her research focuses on understanding the effects of various precision-complexity tradeoffs in the specification of such models on model output, for the purpose of facilitating the construction and explanation of Bayesian networks.
Hengyi Hu, Ph.D., George Mason University
Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.
A Bayesian Network is a popular framework for causal studies and for representing causal relationships among a network consisting of multiple variables. Causal relationships and their associated conditional probabilities can be represented in the structure of a Bayesian Network as nodes and edges, creating a Causal Bayesian Network. However, establishing causality extends beyond learning conditional probabilities from a dataset.
This presentation provides a crash course on the history of establishing causation in epidemiology, current viewpoints on defining causality, and a demonstration of how Bayesian Networks can be used to infer causation. We will examine the criteria for establishing a causal relationship, learning a Bayesian Network from a sample dataset, and augmenting (and improving) a Bayesian Network with informed prior knowledge from an ontology such as ICD-10.
Dr. Hengyi Hu is a data scientist and subject matter expert specializing in advanced analytics, performance analysis, process improvement, and program management. He has over 15 years of experience leading data-driven projects for the Department of Homeland Security, the National Science Foundation, and the Department of Justice. His current role in the Strategic Solutions Office at DHS HQ involves leading in-depth analysis of DHS-wide and government-wide category management spending, revamping specialized procurement reports and procurement reporting systems, and leading cross-agency collaborations for data-driven decision-making in category management.
Hengyi holds a B.S. degree in Information Sciences and Technology from Penn State University and M.S. and Ph.D. degrees in Information Technology from George Mason University. His research interests focus on causality, causal modeling, causal inference, and substantiating Bayesian networks learned from large datasets using causal mechanisms from authoritative ontologies. Hengyi holds certifications for PMP, CSSGB, FAC-COR II, and Strategy & Performance Management. Hengyi is also a graduate of the Key Executive Leadership Program at American University.
Using Bayesian Networks to Extract Expert Knowledge from a Pre-existing Machine Learning Model Trained Elsewhere
ML practitioners have traditionally had to make a choice between the most performant models and those that allow for clear explanations of model decisions. With the recent advent of powerful XAI techniques, this is becoming less of an issue as formerly black box models become (more) transparent. Using these tools on large, complex, real-world tabular datasets, however, can lead to disappointing results that fall short of what is necessary for key stakeholders to use and trust system decisions. Using Bayesian networks, we propose a method that can help build a bridge between machine and expert “reasoning.” We show that applied to the classification of stock market investments – a field with a notoriously low signal-to-noise ratio – this method can help bring the best of man and machine working together to tackle new problems in applied data science.
Workflow automation in Bayesialab with applications to time series analysis (Paris, 2017)
Presented at the on Wednesday, October 26, 2022.
Gabriel Andraos jointly leads Voya’s Machine Intelligence group (part of Voya Financial, a leading health, wealth, and investment company — NYSE ticker: ). As the co-head of VMI, he focuses on research and development in the application of AI and machine learning models for fundamental investing. For more than ten years, the VMI team has been using machine intelligence to run virtual employees – analysts, traders, and portfolio managers with transparent, explainable computer models anchored in fundamentals. Gabriel has approximately 26 years of investment experience. Prior to joining Voya, Gabriel was a managing partner and co-founder of G Squared Capital LLP. Before that, he held senior investment roles in Europe, the U.S., and Asia, combining knowledge and experience in fundamental analysis with the latest tools in computing and data science. Gabriel received an MBA from Harvard Business School and a BA in Economics from Georgetown University. He also has a Certificate in Quantitative Finance and several artificial intelligence, data science, and machine learning accreditations.
Yong Zhang, Ph.D., Procter & Gamble
Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 26, 2022.
We investigated how to conduct driver analysis based on topics derived from unstructured textual data. These data include online consumer reviews, ratings, complaints, comments, and verbatims in surveys. The major challenge is the high missing rate of topics in each individual textual document. As an example, each consumer review may just mention a few topics. This leads to an overall higher missing rate in the data. Without knowing the explicit missing mechanism, BayesiaLab recommended using Approximate Dynamic Imputation (ADI) to impute the missing values. We performed simulations to study different methods of processing missing data and performing driver and impact analysis. With complete and missing simulated data (a mixed missing mechanism), Filtered State and ADI tend to learn the same or very similar model structures, drivers, and impacts. At a low missing rate (~10%), structures, drivers, and impacts are the same as those from the simulated Ground Truth BBN model; at a medium missing rate (40-60%), they also tend to be the same or very similar as GT BBN model through equivalent model structures; at a high missing rate (80%), they tend to recover most of the correct structure, drivers and impacts.
Dr. Yong Zhang leverages Bayesian data and modeling science to develop strategies for product design, manufacturing, storage, and transportation across P&G to improve consumers’ life quality and positively influence the environment and society. He develops first principle and data science/machine learning methods and tools through Front-End Innovation projects to enable and promote the capability across P&G for breakthrough consumer understanding and product innovation. The methods and tools can be used to extract and integrate information from a variety of data sources to find a “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.
Dr. Yong Zhang leverages Bayesian data and modeling science to develop a strategy for product design, manufacturing, storage, and transportation across P&G to improve consumers’ quality of life and drive positive influence on the environment and society under different climate change scenarios. He develops modeling and simulation methods and tools through Front End Innovation projects to enable and promote the capability across P&G for breakthrough consumer understanding and product innovation. The methods and tools can be used to extract and integrate information from a variety of data sources to find a “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.
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Presented at the 10th Annual BayesiaLab Conference on Wednesday, October 26, 2022.
Risk assessment is challenging when data is unavailable, hard to obtain, or costly to process. Organizations often request estimates from experts instead. This talk demonstrates how to integrate cybersecurity data with expert estimates using Bayesian Networks. Cybersecurity analysts, resource managers, and executives can use Bayesian Network models to perform risk assessments, select security controls, and prioritize which suspicious events to investigate first. System administrators can configure autonomous sources of data, including vulnerability scanners and cybersecurity event monitoring systems, to automatically update these hybrid network models alongside inputs from risk analysts and executives.
Corey Neskey, Vice President, Quantitative Risk, Hive Systems corey.neskey@hivesystems.io
Corey has been providing analyses, architecting secure environments, and leading security program implementations in IT security and risk since 2011. His career started with informing executive decision-making using algebraic data analyses for explanation, simulation, and attribution (i.e., intelligence analysis, forensics, SOC, CIRT), and optimization. His toolset expanded to more descriptive and predictive methods (i.e., machine learning/AI for risk assessment, vulnerability prioritization, and event correlation). He is now developing skills for integrating these analytical areas and expanding beyond algebraic methods and static probability calculus to using Bayesian network models.
Presented at the 10th Annual BayesiaLab Conference on Thursday, October 27, 2022.
Recent changes to U.S. accounting rules require estimation, in a continuous-variable sense, of current expected credit losses (CECL) on "financial instruments held at the reporting date, based on historical experience, current conditions, and reasonable and supportable forecasts." This new, continuous treatment of credit losses contrasts with the prior binary accounting rule under which losses were recorded only if "probable." At the same time, the standard for auditing such estimates, known as SAS 143, has also changed, placing emphasis on the effects of uncertainty, subjectivity and judgment, negligent or intentional management bias, complexity, and change. Both the accounting and auditing rules require probabilistic and causal reasoning, for which Bayesian networks are an effective tool. This presentation explores the application of Bayesian networks to the audit of current expected credit losses under the new standards, treating as a target variable the risk of material misstatement (RMM) of the continuous CECL estimate.
Kurt Schulzke, JD, CPA, CFE, teaches accounting information systems, auditing, forensic accounting, risk management, and leadership at the University of North Georgia. His teaching, research, and consulting integrate data science, accounting, and law. He has published on business valuation, economic damages, and Bayesian networks in accounting and auditing in the Columbia Journal of Transnational Law, Vanderbilt Journal of Transnational Law, Tennessee Journal of Business Law, Journal of Forensic Accounting Research, and The Value Examiner. As an attorney, Kurt focuses on business entities, estates, and trusts. MAcc (Brigham Young University), J.D. (Georgia State University), M.S. Applied Statistics (Kennesaw State University).
Presented at the 10th Annual BayesiaLab Conference on Friday, October 28, 2022.
Testing of human skills and abilities is a task that is being repeated frequently in the modern world. In this talk, we will explore an approach to Computerized Adaptive Testing using Bayesian Networks. This concept aims at modeling a student and measuring his/her skills. This effort allows us to create a shorter and more precise test as we are able to ask questions suiting the particular student better. We will also present the effect of a special condition of Bayesian Networks used for this task, monotonicity. The monotonicity condition requires a model to satisfy special conditions placed on its parameters. This condition is especially helpful in cases where the learning dataset is small. We will present a new method for learning monotone parameters. Based on our experiments these models provide better results than non-monotone methods and competitive monotone methods. Monotonicity is an important concept that helps to learn models and allows us to learn more reliable parameters. Monotone models are more likely to be accepted by final users in areas where monotonicity is to be expected.
Martin Planjer is the Director of the Research and Development department in the consultancy company Logio. This department's goal is to keep the company at the technological edge and to provide new methods and methodology. This is done by seeking for new approaches, prototyping, and defining new products.
Martin is also a junior researcher at the Institute of Information Theory and Automation (UTIA) in the field of decision-making theory with mathematical modeling background from Ph.D. studies and the Czech Technical University.
These two parts provide an opportunity to combine the business and the academic world and to challenge both theoretical concepts as well as established practices.
Presented at the 10th Annual BayesiaLab Conference on Thursday, October 27, 2022.
Radiation dose in nuclear power plant reactors is known to be dominated by the presence of radioisotopes in the primary loop of the reactor. To strictly control it in normal operation (e.g., cleaning and reloading of nuclear fuel), established chemical theories exist to explain the amount of radioisotopes present in the reactor water circuits with respect to known control variables in the plant (e.g., thermal power on the reactor, pH, hydrogen, etc.). However, the high complexity and the uncertainty of the process make difficult an accurate estimation of the measured values of radioisotopes. In order to address this problem, this article introduces a dynamic Bayesian network (DBN) probabilistic model that allows to experimentally demonstrate the capabilities of the control variables to give information about the value of the radioisotope concentrations and to predict their values in a data-driven way. Our results in 5 different nuclear power plants show that the accuracy and reliability of these predictions are remarkable, enabling strategies for gathering reliable information about the chemical process in the primary loop towards possible operational improvements.
Dr. Daniel Ramos finished his Ph.D. in 2007 at Universidad Autonoma de Madrid (UAM), Spain. Since 2011, he has been an Associate Professor at the UAM. He is a staff member of AUDIAS Group. During his career, he has visited several research laboratories and institutions around the world, including the Institute of Scientific Police at the University of Lausanne (Switzerland), the School of Mathematics at the University of Edinburgh (Scotland), the Electrical Engineering School at the University of Stellenbosch (South Africa), and more recently the Netherlands Forensic Institute and the Computational and Biological Learning Lab of the University of Cambridge. He has been visiting professor at the Universidad de Buenos Aires in 2019. His research interests focus on the forensic evaluation of the evidence using Bayesian techniques, probabilistic calibration, validation of forensic evaluation methods, speaker and language recognition, and, more generally, signal processing and pattern recognition. Dr. Ramos is actively involved in the research of development of different aspects of forensic science, including the statistical evaluation of speech and chemical evidence (mainly glass). He has been invited by the NIST to several workshops, including the OSAC standardization initiative. He is the author of multiple publications in national and international journals and conferences, some of them awarded. He has also participated in several international competitive evaluations of speaker and language recognition technology since 2003. Recently, he has been working on signal processing and machine learning for industrial applications in the energy sector. Dr. Ramos is regularly a member of scientific committees at different international conferences, and he is often invited to give talks at conferences and institutions.
Presented at the 10th Annual BayesiaLab Conference on Friday, October 28, 2022.
GM’s experience shows that expanded data access must be coupled with tools that can manage volume, techniques that appropriately control for exposure and sample bias, and processes that incorporate SME knowledge. BayesiaLab helps GM address these critical issues, with one tool, in a way that is intuitive and makes communication with engineers easier to support vehicle hazard investigations.
Michelle Michelini is currently a Senior Technical Fellow in the Global Planning Analytics team at General Motors, where she focuses on projects which require the deployment of new analytical methods and tools to develop deeper insights and recommendations from Planning data. Michelle started her career as a statistician with GM Credit Card and then worked for OnStar as a strategist. From OnStar, she went to GM Vehicle Safety, where she developed the Vehicle Safety Analytics team as a response to the findings of the ignition switch investigation. She holds a Bachelor of Science from the University of Michigan in Mathematics and Applied Statistics and a Master of Science from Carnegie Mellon University in Information Technology and Systems.
A Zoom Virtual Event — October 11–15, 2021
Since Judea Pearl first proposed Bayesian networks in the 1980s, this new paradigm's attractive mathematical and statistical properties have become well-understood and widely utilized within computer science. However, their enormous potential as a practical research framework—beyond computer science—has only emerged more recently. Promoting the practical use of Bayesian networks for research, analytics, and reasoning is the principal objective of our annual BayesiaLab Conference.
For the last eight years, this event has provided unique opportunities to learn about the state of the art in practical applications of Bayesian networks, from architecture to zoology and everything in between. Many of our regular conference attendees appreciate the cross-disciplinary nature of the program, and you may find that an innovative methodology from medical research could very well apply to a marketing science problem or vice versa.
If you missed some of the talks, we uploaded recordings of all presentations and the corresponding slides.
Dr. Lionel Jouffe, CEO, Bayesia S.A.S.
Presented at the 9th Annual BayesiaLab Conference on October 11, 2021.
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
A latent-observational space analytical formalism is applied to a sub-grid modeled turbulent kinetic energy (tke) field emanating from ocean turbulence large eddy simulation (LES) data containing Langmuir cells but no breaking waves. The purpose of the analysis is to illustrate how machine learning modeling can be used to understand the probabilistic structure of observational space and how the allied latent space can be related statistically to it for the purpose of data generation. The Peter and Clark (PC)-algorithm-based Bayesian belief network (BBN) edge-nodal structure for the observational-space tke subdomains demonstrates a distinctive nonlocal connectivity pattern when the multidimensional scaling graph layout is invoked. When the Chow-Liu algorithm is used, tree-based connectivity in the network is revealed. In particular, a dominant parental root node occupies the far upper left region in the observational-space domain with many edge connections flowing toward the right. Hidden Markov model (HMM) parameter estimation, applied to the maximum and minimum values taken from the mean tke feature matrix and to generative topographic mapping (GTM)-based latent space for the same tke feature nodes, enables estimation of the latent-space state transition matrix and tke latent-observational space emission matrices. The latent-space state transition matrix demonstrates how many columns of latent space, associated with different root-mean-square tke values, possess a high probability of transitioning to other distinct latent-space areas. The tke latent-observational space emission matrix provides spatial subdomain locations most strongly tied to specific vertical columns of GTM-based latent space. The maximum value-based emission matrix shows very high probabilities for single columns on the latent-space perimeter being associated with maximum values occurring at specific observational-space subdomain locations. These observational-space nodal locations have strong statistical linkages to other nodes when the Chow-Liu-algorithm-based BBN is invoked, suggesting this model to be physically appropriate to the high-energy turbulent flow physics. Bayesian and manifold learning processing methods provide a way for understanding the spatial structure of LES-derived tke features and how latent space can provide a constraint for discerning optimal linkages between spatially separate observational-space subdomains.
Dr. Nicholas Scott is a modeling scientist and physical oceanographer and has been a member of the professional staff at Riverside Research in Dayton, Ohio, since October 2012. He investigates the applicability of non-traditional signal and image processing techniques to the extraction of information from remotely sensed data. This includes hyperspectral imagery. His present work includes statistical modeling of computer network system traffic, Bayesian analysis of geo-intelligence system nonlinear dynamics, and time series analysis of environmental data. He also exploits probabilistic graphical modeling algorithms for understanding the structure existing within turbulent flow imagery features and numerically simulated data.
Presented at the on Friday, October 28, 2022.
Economic growth in most advanced countries is driven by small and medium enterprises, and most countries prioritize entrepreneurship for economic growth and innovation. This is very apparent in the United Arab Emirates, where an average of about 39% of adults want to start a business in the next three years. As such, Entrepreneurial intentions have been a major focus of research, but they have always been studied using generic models. We use Bayesian Networks (BN) to model entrepreneurial intentions as it provides an advantage over classical methods. To our knowledge, no study has used the BN framework to model entrepreneurial intentions within the UAE. Using the Theory of Planned Behavior (TPB) as a foundation, a cross-sectional study was conducted among a random sample of 324 Emirati University students in the UAE. We implemented Unsupervised Structural learning within BayesiaLab using the SopEQ unsupervised algorithm to minimize the “Minimum Description Length” (MDL) score. Our model provides confirmation of and more robust statistical support for existing theoretical frameworks. It helped not only find relationships among the different entrepreneurial factors but also assess the effects of changes in these variables on intentions. One of the strengths of our study is the inclusion of attitudes toward entrepreneurship and self-efficacy variables. Accordingly, the main conclusion that can be drawn from our model is that Entrepreneurial intentions are highly affected by attitude, self-efficacy, subjective norms, and opportunity feasibility. The results can be used by professionals for proposing new policies for university opportunities and government support.
Linda Smail, Ph.D. Department of Mathematics & Statistics College of Natural and Health Sciences Zayed University, Dubai, United Arab Emirates linda.smail@zu.ac.ae
Linda Smail is an Associate professor in the Department of Mathematics and Statistics at Zayed University, Dubai, United Arab Emirates, where she teaches Mathematics and Statistics courses. She obtained her Ph.D. in Mathematics from Marne-La-Vallée University, France, in 2004. Her research interests are in inference, learning graphical models, and applications of Bayesian Networks in different fields, from Education to Health.
Presented at the 9th Annual BayesiaLab Conference on October 11, 2021.
Long-term brand marketing is important to achieve sustainable growth. There are many important areas that companies can invest in paid ads, performance marketing, and affiliate marketing. They will all help with growth, but eventually, you will hit that glass ceiling. Plus, with nearly every market oversaturated at this point, we will need to spend a whole lot of money to have any chance of standing out.
In my talk, I demonstrate how Course5 is disrupting the space of Digital Media Optimization with its new solution offering and why it is important for the industry to consider and adopt such solutions in the challenging times we are in.
Anand Wilson, Lead Data Science Consultant, Advanced Analytics Course5 Intelligence
Anand comes with 9+ years of experience in applied artificial intelligence and data sciences. He has worked for marque clients such as Lenovo, Intel, Microsoft, Novartis, Novo Nordisk, GE, Mars Wrigley, PepsiCo, etc., enabling digital transformation using A.I.
In his current role, Anand focuses on developing and market solutions based on the Bayesian Network model theory, which enables us to quantify the causality in an observational study. A major area of work/research includes Knowledge Modelling, Machine Learning with BayesiaLab, and Inference.
Anand comes from applied statistics background. He has a master's degree in statistics. Anand carries an acute interest in machine reasoning, causal inference, and experimental designs, along with machine learning and data science.
Buvana Iyer, Principal Solution Architect, Discovery Solution Course5 Intelligence
Buvana does consult for C-level clients and has led projects achieving org-level implementation of enterprise analytics, including Software Development, BI & Analytics, and ML & AI solutions (at scale), adopting DevOps philosophy with agile delivery. Expertise in leveraging both traditional statistics and machine learning techniques to create solutions and deliver business value.
Buvana comes from applied Mathematics background. She has a master's in Mathematics. Buvana carries an acute interest in Predictive Analytics, Statistical modeling, machine reasoning, and experimental designs, along with machine learning and data science.
Customer Preference Sequencing for Better Customer Engagement (Laval Virtual World, 2020)
Modern Approaches to Causal Modelling in Customer Experience Measurement (Durham, 2019)
To deal with increasing amounts of data, decision and policymakers frequently turn to advances in machine learning and artificial intelligence to capitalise on the potential reward. But there is also a reluctance to trust black-box models, especially when such models are used to support decisions and policies that affect people directly, like those associated with transport and people's mobility. Recent developments focus on explainable artificial intelligence to bolster models' trustworthiness. In this paper, we demonstrate the use of an explainable-by-design model, Bayesian Networks, on travel behaviour. The model incorporates various demographic and socioeconomic variables to describe full-day activity chains: activity and mode choice, as well as the activity and trip durations. More importantly, this paper shows how the model can be used to provide the most relevant explanation for people's observed travel behaviour. The overall goal is to show that model explanations can be quantified and, therefore, assist policymakers to truly make evidence-based decisions. This goal is achieved through two case studies to explain people's vulnerability as it pertains to their total trip duration.
Alta de Waal, Ph.D. BMW Software Factory South Africa
Alta is a senior data scientist at the BMW Software Factory in South Africa. She has more than 20 years of experience in the design, development, and implementation of different components in the AI value chain. Her current research focus is natural language processing (NLP) and explainable methods in AI for the purpose of actionable insights, fairness, and accountability in these systems.
Bayesian Networks for Knowledge Discovery and Curriculum Optimisation in Academic Programmes (Laval Virtual World, 2020)
Activity-Based Travel Demand Generation Using Bayesian Networks (Laval Virtual World, 2020)
Spatially Discrete Probability Maps for Anti-Poaching Efforts (Paris, 2017)
Presented at the 9th Annual BayesiaLab Conference on October 12, 2021.
Bayesian networks allow us to uniquely visualise data and tackle complex interdisciplinary problems. Bayesian networks are based on Bayes' theorem. The premise of this theory is that initial (prior) beliefs can be updated based on new evidence. Part of the appeal of this method is its intuitive nature. The process of updating beliefs, given new information, is common to everyday scenarios. Bayesian networks can be used for variable inference (identifying the value of variables), parameter inference (identifying probabilistic dependencies between variables), and structure learning (understanding associations among variables). Social science is an area with large amounts of complex interdisciplinary data where Bayesian networks may be useful to unravel relationships among variables. However, the uptake of Bayesian networks in social science is relatively low. Here, we look at how Bayesian networks have been applied to antibiotic resistance and antimicrobial use and explore potential barriers to their use in this field of study. The complex nature of this biosocial phenomenon means that applications are increasingly making use of social science data, e.g., survey data. This type of data is often associated with high levels of missing data. Here, we further consider how this missing data can be addressed for Bayesian network structure learning. We compare a commonly used method in social science, multiple imputation by chained equations (MICE), with one specific for Bayesian network learning, structural expectation-maximization (SEM). We simulate multiple incomplete data sets with different missingness mechanisms, numbers of categorical variables, and amounts of missing data. We evaluate and compare the performance of MICE and SEM in capturing the real Bayesian network structure under each condition. We find that applying either method (MICE or SEM) provides better structure recovery than doing nothing, and SEM, in general, outperforms MICE. This finding is robust across missingness mechanisms, the number of variables, and the amount of missing data. This suggests that taking advantage of the additional information provided by network structure during SEM can improve the performance of Bayesian networks for social science and other interdisciplinary analyses.
Madeleine Clarkson Irvine Building University of St Andrews St Andrews, KY16 9AL, Fife, UK mcc23@st-andrews.ac.uk
Ms. Madeleine Clarkson has an undergraduate degree in Economics from the University of Cape Town, South Africa, and an MSc in the Control of Infectious Disease from the London School of Hygiene and Tropical Medicine(LSHTM), United Kingdom. She has worked as a research assistant in infectious disease modeling at Imperial and LSHTM. She is currently undertaking a Ph.D. in Bayesian Network analysis of Antimicrobial resistance at the University of St Andrews based within Dr. V Anne Smith's Lab.
Xuejia Ke Harold Mitchell Building University of St Andrews St Andrews, KY16 9TH, Fife, UK xk5@st-andrews.ac.uk
Ms. Xuejia Ke has an undergraduate degree in Pharmacy from China Pharmaceutical University, China, and an undergraduate degree in Pharmacology and Biochemistry from the University of Strathclyde, United Kingdom. She has an MSc in Bioinformatics from the University of Edinburgh, United Kingdom. She has worked on statistical models and software for RNA-seq quantification from subcellular fractions in her MSc project. She is currently undertaking a Ph.D. in Bayesian Network analysis of social science data at the University of St Andrews within Dr. V Anne Smith's lab.
Presented at the 9th Annual BayesiaLab Conference on October 13, 2021.
Bayesian networks are a very powerful tool to better understand the links between the living environment of the population and its health. The study reported here investigated the potential relationships between air pollution, socio-economy, and proven pathologies (e.g., respiratory, cardiovascular) within an industrial area in Southern France (Etang de Berre), gathering steel industries, oil refineries, shipping, road traffic, and experiencing a Mediterranean climate. A total of 178 variables were simultaneously integrated within a Bayesian model at an intra-urban scale. Unsupervised and supervised algorithms (maximum spanning tree, tree-augmented naive classifier), as well as sensitivity analyses, were used to better understand the links between all variables and highlighted correlations between population exposure to air pollutants and some pathologies. Adverse health effects (bronchus and lung cancers for 15–65 years old people) were observed for hydrofluoric acid at low background concentration (<0.003 μg m−3) while exposure to particulate cadmium (0.210–0.250 μg m−3) disrupts insulin metabolism for people over 65 years-old leading to diabetes. Bronchus and lung cancers for people over 65 years old occurred at low background SO2 concentration (6 μg m−3) below European limit values. When benzo[k]fluoranthene exceeded 0.672 μg m−3, we observed a high number of hospital admissions for respiratory diseases for 15-65 years-old people. The study also revealed the important influence of socio-economy (e.g., single-parent family, people with no qualification at 15 years old) on pathologies (e.g., cardiovascular diseases). Finally, diffuse polychlorinated biphenyl (PCB) pollution was observed in the study area and can potentially cause lung cancers.
Sandra Pérez, Ph.D. UMR ESPACE 7300 98, Bd E. Herriot BP 3209 06204 Nice Cedex France sandra.perez@univ-cotedazur.fr
Sandra is an associate professor in geography at the University of Cote d'Azur. She has been conducting research in environmental health for 15 years, more specifically on the pathogenic potential of geographic spaces.
Presented at the 9th Annual BayesiaLab Conference on October 13, 2021.
Acute myocarditis is an inflammation of the myocardium. It can occur at any age and has no typical presentation. Myocarditis is also an important cause of morbidity and mortality. Moreover, there is no current prognosis score existing.
The objective of this research was to predict and quantify the risk of cardiovascular events defined by extracorporeal membrane oxygenation (ECMO), heart transplant, or death in acute myocarditis patients.
Using the AMPHIBIA registry, we developed a Bayesian network model to create a prognostic score. This Score quantifies the probability for a patient to reach our composite endpoint. We decided to exclusively retain baseline data while excluding all further data to predict early detection of the event. With the Bayesian theorem, we can draw a representation of our variables' dependencies, namely a Bayesian network. More precisely, a Bayesian network is a directed acyclic graph representing variables by a node and their conditional dependencies by a direct link. To create our predictive model, we first needed to discretize our continuous variables as the Bayesian networks require. Then, we used a supervised learning algorithm: the Markov Blanket. After defining our target variable, the Market Blanket only keeps strongly related variables (p-value << 0.01 in our model), excluding all other variables for the prediction of the event.
Our model shows good performances using only 6 variables from our dataset with an area under the curve of 91% for predicting whether or not the patient will reach the endpoint. With the cross-validation method, the model also performs well, with an area under the curve of 90%. The clinician can directly use the prediction output to classify the patients at their entrance to consider low, medium, and high-risk patients and send them to the appropriate hospital department: standard or intensive care unit. Finally, looking at the posterior probabilities, the patients who will most likely reach the endpoint are women with pre-cardiogenic shock, high NTprobnp, high creatinine, low TP, and no chest pain. Conversely, the patients who won’t reach the endpoint are more often men with chest pain, no cardiogenic shock, high TP, low NT pro-BNP, and low creatinine.
My name is Gatien Hubert, and I am a fourth-year medical student at Sorbonne University, France.
At the same time, I have also followed statistics courses, still at Sorbonne University. As part of this course, I have done an internship in the INSERM Department of Cardiology at La Pitié Salpêtrière Hospital, where I enjoyed using Bayesialab to develop my models.
Presented at the 9th Annual BayesiaLab Conference on October 13, 2021.
In the oil and gas industry, materials employed in upstream operations and pipelines operate at elevated pressures and temperatures, while exposed to substantial concentrations of corrosive agents such as chlorides (Clˉ), carbon dioxide (CO2), and hydrogen sulfide (H2S). Such aggressive conditions give rise to different failure mechanisms associated with environmentally assisted cracking (EAC); predominately stress corrosion cracking (SCC). This corrosion phenomenon is considered a critical threat for production systems, as it can accelerate the mechanical failure of components due to the combined influence of non-cyclic stresses (i.e., residual, external, or operational), and corrosion-oxidation reactions in a reactive environment.
Due to their high mechanical properties and low corrosion rates, the use of corrosion-resistant alloys (CRAs), such as duplex stainless steel (DSS) alloys, have increasingly been employed to improve the integrity of production equipment and transportation facilities. However, the performance of DSS in the hydrocarbon recovery is not well documented in industry standards, such that the operating limits of DSS are often perceived as overly conservative. Thus, the susceptibility of DSS alloys to SCC is actively being investigated to optimize the material selection processes, and thereby ensure the reliability of hydrocarbon production systems. Nonetheless, despite the numerous investigations devoted to describing the SCC phenomenon, a thorough understanding of SCC mechanisms remains elusive.
Given the stochastic nature of failures by corrosion phenomena and the numerous factors involved in SCC, we focus on the implementation of Bayesian networks (BNs) to establish an explanatory framework for the SCC of DSS in downhole environments. Here, we report the initial stage of a BN model, where we exploit the advantage of BNs to reconcile various sources of information sources (e.g., literature relevant to SCC, results from atomistic simulations, experimental data) within one overall framework.
Additionally, we present the use of machine learning (ML) techniques that assisted in the elucidation of the BN configuration. To treat the uncertainty in our dataset due to unobserved data points, we employ advanced data imputation methods (e.g., tree-based models), together with the expectation and maximization (EM) algorithm, and entropy-based models in BayesiaLab. Future plans will also be detailed for which this BN model is intended to predict both the damage stages of DSS, and safe operating thresholds in downhole environments.
Abraham Rojas Zuniga M.Phil., Ph.D. Candidate Faculty of Science and Engineering Western Australian School of Mines and Curtin Corrosion Centre, Curtin University abraham.rojaszuniga@research.uwa.edu.au
I am a Petroleum Engineer with five years of industry experience. I am particularly interested in the application of various simulation techniques, from deterministic to data-driven approaches, to study different phenomena associated with material science and risk assessment.
Presented at the 9th Annual BayesiaLab Conference on October 12, 2021.
I am Gilles Voiron, a research engineer and Geo Data Scientist, an expert in electric mobility. My job is to carry out studies based on precise data to help and advise businesses and communities in the ecosystem of electric mobility. The studies carried out considering local specifications allow lessons to be learned to propose recommendations for today and to plan the needs in the years to come through geo-prospective models on the horizon 2025-2030-2035.
Given wide plausible value ranges, the greatest value that a business valuation expert offers a client may be the ability to persuade others (e.g., judges) to locate their preponderance of probabilities (evidence) across the client’s interval within the plausible value range. Accomplishing this feat is a function of technical valuation expertise, as well as communication tools and techniques. This presentation explores Bayesian networks as a platform for facilitating the probabilistic estimation, negotiation, and communication of business value.
Kurt S. Schulzke, JD, CPA, CFE Associate Professor of Accounting & Law University of North Georgia kurt.schulzke@ung.edu
Kurt Schulzke, JD, CPA, CFE, teaches forensic accounting and audit analytics at the University of North Georgia. He has published on revenue recognition, materiality, expert witnessing, economic damages, and business valuation through a Bayesian networks lens in a variety of outlets, including the Columbia Journal of Transnational Law, Vanderbilt Journal of Transnational Law, Journal of Forensic Accounting Research, Tennessee Journal of Business Law, and The Value Examiner. With an M.S. in Applied Statistics from Kennesaw State University, he is equally adept as counsel, expert witness, or neutral in valuation-related matters.
Presented at the 9th Annual BayesiaLab Conference on October 13, 2021.
In the public works environment, avoiding the breakdowns of construction machines is a major challenge. Indeed, this phenomenon can represent a significant economic cost at three different levels. First, we need to pay for the repair of the machine, which is called the direct cost. Then, a breakdown will eventually lead to a delay in the progress of the work or to the need to rent another machine to replace the one that is unavailable, all this representing the indirect cost. And finally, a breakdown can also affect the lifetime of a machine, and optimizing this lifetime is a priority when handling a fleet of public works equipment.
In order to reduce the number of breakdowns, our goal is to develop a system of predictive maintenance, comparably to what can be used in the industry, using the telematic data that the machines produce.
After testing different “data-driven” approaches and given the complexity and diversity of the breakdowns that can occur, we decided to focus on one specific component: the hydraulic system of crawler excavators, using an expert-based approach with BEKEE to build a Bayesian network representing the health of a hydraulic system.
Yann Corriou Data Scientist Charier S.A.S. yann.corriou@charier.fr
I studied for five years (2015-2020) at INSA Rennes (engineering school) in the Department of Applied Mathematics. After an internship (2019) and a one-year work-study contract (2019-2020), I am now working full-time as a data scientist at CHARIER, a public works company operating mainly in the West of France.
Presented at the 9th Annual BayesiaLab Conference on October 13, 2021.
It is a challenge to cluster and segment data in high-dimensional space. Traditional clustering methods relying on distance (e.g., k-means) or density (DBScan) generally fail to identify meaningful clusters in high dimensional space. We investigated clustering methods in high-dimensional space using Bayesian Belief Network (BBN) models, k-means, Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Polytomous variable Latent Class Analysis (PoLCA), and Profile Regression. These methods were used to cluster a set of users and prospective users of Setlist Beauty, which is a digital iPhone App owned by P&G. There are around 500 variables to describe these users. We found that the BBN model performs very well in high-dimensional clustering. Most importantly, it provides metrics to inform us what variables/questions can differentiate consumers and what answers to these questions characterize a consumer segment. These segments and metrics helped deliver actionable insights for targeted advertisement, acquisition, and App feature improvement, etc.
Dr. Yong Zhang leverages Bayesian data and modeling science to develop strategies for product design, manufacturing, storage, and transportation across P&G to improve consumers’ life quality and drive positive influence on the environment and society. He develops first principle and data science/machine learning methods and tools through Front End Innovation projects to enable and promote the capability across P&G for breakthrough consumer understanding and product innovation. The methods and tools can be used to extract and integrate information from a variety of data sources to find a “Body of Evidence” for consumer and product research based on Nonparametric Bayesian statistics and deep learning algorithms.
Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.
Comprehension of computer network traffic structure is an important part of geo-intelligence information technology’s quest to safeguard computer systems that support important areas of research and defense. Of crucial importance is the need to understand the structure of computer network traffic patterns, which are crucial to building complex algorithms that defend against computer system intrusion and attack. Bayesian belief networks and machine learning are applied to open-source computer network traffic data to develop algorithms relevant to exhuming latent patterns and modeling computer server state changes. In particular, manifold learning and Bayesian statistical methods are applied to a multidimensional data set to explore whether a two-tier analytical approach based on statistical characterization and modeling is appropriate. Preliminary statistical analysis shows which server sites from a ten-dimensional array experience a high probability of attack. Results also reveal a pattern where certain computer server sites are connected, which in turn provides a guide as to where cyber security resources should be placed to support computer network health. The structural simplicity of the developed algorithmic array offers a rigorous but flexible methodology applicable to a variety of cyber defense systems.
Nicholas V. Scott, Ph.D. Riverside Research Institute Open Innovation Center 2640 Hibiscus Way Beavercreek, OH 45431 nscott@riversideresearch.org
Dr. Nicholas Scott is a modeling scientist and physical oceanographer and has been a member of the professional staff at Riverside Research in Dayton, OH, since October 2012. He investigates the applicability of traditional and non-traditional signal and image processing techniques to extracting information from remotely sensed imagery. This includes hyperspectral and multispectral imagery. His present work includes statistical modeling of geo-intelligence information, sensor array time series analysis of environmental data, and applying pattern recognition techniques to turbulent flow imagery and numerically simulated data. He is also involved in applying probabilistic graphical modeling algorithms for information fusion and statistical inference.
Jack McCarthy Duke University, Dept. of Statistical Science jack.mccarthy@duke.edu
Spatio-temporal Multicomponent Optimal Learning State Estimation of Direct Numerically Simulated Turbulent Features: A Smart Sensing Approach (Laval Virtual World, 2020)
Bayesian Structural Field Analysis (Durham, 2019)
Bayesian Network Modeling of Imagery Features From Direct Numerically Simulated Turbulent Sediment-Laden Oscillatory Flow (Chicago, 2018)
A Strategic Approach to Probabilistic Networks in Poultry and Stress
Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.
Exploring publicly available genetic data repositories, such as Gene Expression Omnibus or Array Express, represents a great possibility to collect data previously published and get a deeper insight into a particular field of genetics. In the field of poultry genetics, experimental designs evaluate only a relatively small number of birds per study, requiring the combination of multiple sources into one bigger dataset for further analysis, focusing on one variable of interest, such as stress. Bayesian networks are a useful tool to overcome this challenge, as they can deal with uncertainty and noise resulting from different experimental designs, discovering relationships that are not necessarily linear. Therefore, our goal was to identify genes associated with stress in chickens, invoking an approach to Bayesian networks that involved the identification of genes of interest, the reduction of the dimensionality, followed by the learning of the structure of the consensus Bayesian network. Initially, genes identified in a previously published study were extracted from two other datasets with a similar experimental design. Our dataset consisted of 50 chickens, 101 genes and their expression values, and the stress condition. As the number of genes was rather too large to apply Bayesian networks algorithms directly, a supervised Naïve Bayes algorithm was implemented. The top 10 genes that contributed the most to the stress condition were used to learn the structure of the Bayesian network by the software Banjo to search for the best consensus network. Our results showed that all genes, as well as the condition, were included in the overall structure of the consensus network, indicating that all were interconnected. Interestingly, WNT7A, the gene that contributed the most to the condition according to Naïve Bayes, was found in close association with it in the network. Additionally, HSPH1 also displayed a relationship with the condition. The discovery of these two genes could be further explored in future studies as genes related to stress resistance or stress resilience with the aim of improving the welfare of chickens bred under commercial environments.
Emiliano Ariel Videla Rodríguez School of Biology University of St Andrews St Andrews, Fife KY16 9TH United Kingdom
Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.
Risk assessment is challenging when data is unavailable, hard to obtain, or costly to process. Organizations often request estimates from experts instead. This talk demonstrates how to integrate cybersecurity data with expert estimates using Bayesian Networks. Cybersecurity analysts, resource managers, and executives can use Bayesian Network models to perform risk assessments, select security controls, and prioritize which suspicious events to investigate first. System administrators can configure autonomous sources of data including vulnerability scanners and cybersecurity event monitoring systems to automatically update these hybrid network models alongside inputs from risk analysts and executives.
Corey Neskey Vice President, Quantitative Risk Hive Systems corey.neskey@hivesystems.io
Corey has been providing analyses, architecting secure environments, and leading security program implementations in IT security and risk since 2011. His career started with informing executive decision-making using algebraic data analyses for explanation, simulation, and attribution (i.e., intelligence analysis, forensics, SOC, CIRT), and optimization. His toolset expanded to more descriptive and predictive methods (i.e., machine learning/AI for risk assessment, vulnerability prioritization, event correlation). He is now developing skills for integrating these analytical areas and expanding beyond algebraic methods and static probability calculus to using Bayesian network models.
Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.
Understanding the background metal concentrations of soils is important for setting remedial goals at polluted sites. To better understand urban background concentrations for contaminated site remediation and risk assessment, personnel from Region 4 at the U.S. Environmental Protection Agency led a collection and analysis effort for urban soils in five states of the southeastern U.S. Each of the cities within these states had 50 samples collected from randomly chosen grid cells with additional qualifying criteria for within-grid cell sampling. Seven cities in these five states were included in the current Bayesian network analysis (Gainesville, FL; Lexington, KY; Louisville, KY; Raleigh, NC; Winston-Salem, NC; Columbia, SC; and Memphis, TN). Chemical concentration data frequently contain analyzed values that are considered non-detected data. These data are often assumed to have a potential concentration that ranges from 0 to the method detection limit of the analysis. Preliminary work examined the influence of substitution for case file usage on discretization thresholds for these non-detected data. The final metals chosen for analysis and other urban site measurement data were condensed into a single case file with each case representing one sampling site with columns for concentrations of metals, coordinates, land use, nearby emission sources, city, and state information for each sampling site. Data clustering with expectation-maximization was used to create a new factor variable with cluster states based on the metals data from all cities. Relationships between the identified metals concentration clusters and nodes from the case file that were excluded from the clustering analysis (cities, nearby emission sources, and land use) were also examined. These analyses explored the relationship of different sampling site characteristics with the metals clusters through sensitivity analyses and probability distribution changes. Data clustering analysis can be useful for interpreting and exploring background metals concentration sampling data for urban regions.
EPA Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
John F. Carriger U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation Technology Division, Environmental Decision Analytics Branch, Cincinnati, OH
Robert G. Ford U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation Technology Division, Contaminated Sites and Sediments Branch, Cincinnati, OH
Tim Frederick Sydney Chan U.S. Environmental Protection Agency, Region 4, Superfund and Emergency Management Division, Resource and Scientific Integrity Branch, Scientific Support Section, Atlanta, GA
Yuen-Chang Fung Tetra Tech, Inc.
John Carriger is a researcher with the U.S. Environmental Protection Agency’s Office of Research and Development. John received his Ph.D. in Marine Science from the College of William & Mary in 2009. His research interests are developing and applying causal modeling, decision analysis, and risk assessment tools to diverse environmental problems. John lives and works in Cincinnati, OH, USA.
One of the most challenging tasks when building Bayesian networks with a group of subject matter experts is the parametrization of the model, i.e., the quantification of the probabilistic relationships. Indeed, with the default tabular representation of Conditional Probability Distributions (CPDs), the number of probabilities to be elicited grows exponentially with respect to the number of parent nodes.
As a result, there is not only a problem regarding the time it takes to fill in these large Conditional Probability Tables (CPTs) but also a problem of consistency between the elicited probabilities.
Even though formulas offer an effective way to fill CPTs, it is not really possible to use them "live" in a brainstorming session because they can seem quite complex and abstract to experts. An elegant alternative approach is determining whether the model requires considering all interactions between the parent nodes. If not, we can "divorce" the parent nodes and effectively represent their Independent Causal Influence (ICI) on the child node.
BayesiaLab 10 includes a new tool for automatically generating different types of ICI models. Experts can choose the Combination Function (Or, And, Max, Min, Sum, Vote, Average) and then elicit Local Causal Effects using simple counterfactual questions.
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
Presented at the on October 15, 2021.
ICI models are not only useful for modeling expert knowledge but can also be useful in the context of . The model described above is indeed a Bayesian Regression Model. When data is available, can be utilized to automatically estimate the Local Effects.
Imagine that two doctors in the same city give different diagnoses to identical patients—or that two judges in the same courthouse give markedly different sentences to people who have committed the same crime. Suppose that different interviewers at the same firm make different decisions about indistinguishable job applicants—or that when a company is handling customer complaints, the resolution depends on who happens to answer the phone. Now imagine that the same doctor, the same judge, the same interviewer, or the same customer service agent makes different decisions depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical.
In Noise, Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein show the detrimental effects of noise in many fields, including medicine, law, economic forecasting, forensic science, bail, child protection, strategy, performance reviews, and personnel selection. Wherever there is judgment, there is noise. Yet, most of the time, individuals and organizations alike are unaware of it. They neglect noise. With a few simple remedies, people can reduce both noise and bias, and so make far better decisions.
Packed with original ideas, and offering the same kinds of research-based insights that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment—and what we can do about it.
Olivier Sibony is a professor, writer, and advisor specializing in the quality of strategic thinking and the design of decision processes. Olivier teaches Strategy, Decision Making, and Problem Solving at HEC Paris. He is also an Associate Fellow of Saïd Business School at Oxford University.
Before he was a professor, Olivier spent 25 years with McKinsey & Company in France and in the U.S., where he was a Senior Partner. There, he was, at various times, a leader of the Global Strategy Practice and of the Consumer Goods & Retail Sector.
Olivier’s research interests focus on improving the quality of decision-making by reducing the impact of behavioral biases. He is the author of articles in various publications, including “Before You Make That Big Decision,” co-authored with Nobel Prize winner Daniel Kahneman, which was selected as the cover feature of Harvard Business Review’s book selection of “10 Must-Reads on Making Smart Decisions”. In French, he also authored a book, Réapprendre à Décider.
Olivier builds on this research and on his experience to advise senior leaders on strategic and operational decision-making. He is a frequent keynote speaker and facilitator of senior management and supervisory board meetings. He also serves as a member of corporate, advisory, and investment boards.
Olivier Sibony is a graduate of HEC Paris and holds a Ph.D. from Université Paris-Dauphine.
Presented at the 9th Annual BayesiaLab Conference on October 15, 2021.
Using Natural Language Understanding (NLU) on millions of texts and optimization, we automatically generate Bayesian Networks centered around financial targets such as the “USA inflation” or “ExxonMobil profits” with strong predictive capabilities. These Bayesian Networks are then loaded into the BayesiaLab simulation tool to enable the testing of various hypotheses on the future evolution of any of the drivers of the specified target. These capabilities can be applied to publicly listed companies, commodities, or macroeconomic indicators.
Dr. Olav Laudy, Chief Data Scientist, Causality Link Dr. Pierre Haren, CEO, Causality Link
Presented at the 9th Annual BayesiaLab Conference on October 15, 2021.
We propose a decision support system that introduces “supervised elicitation,” an approach in machine learning and AI for elicitation practices. Thanks to a semi-automatic initialization of the causal analysis process, it alleviates the domain experts' workload and shortens the duration of iterative analysis, producing a disruptive innovation.
Supervised elicitation involves BayesiaLab earlier in the process, coupled with complementary methods borrowed from network science. Iteratively applying the method to a dataset of about 700 variables, we retained 100 decisive variables elicited for causal analysis.
The team IQ&AI implemented supervised elicitation for a multinational company willing to obtain an accurate and global insight into its performance factors from high-dimensional and sparse data sets.
Joel Païn has held managing positions all along his 25+ year career. More specifically, he created and managed two firms (Evaneo and Up & Up) and has been the CEO of several companies and investment firms (Positive Planet, CroiSens, SPA, FinanCités…). He has also acquired extensive experience in strategy consulting and restructuring: he has led many strategy analysis, strategy, and restructuring consulting assignments (with Deloitte, EY, and Up & Up). On these occasions, he has had the opportunity to measure the gap between the way consulting firms deliver strategic consultancy and the kind of answers and level of quality of service clients really expect to receive. He is convinced that bridging this gap is an issue, that can be at least partially solved thanks to new methodologies (IQ & AI), based on AI, experts, and Bayesian networks.
In 1984, Christophe Thovex started software programming while studying music in Paris, his first professional career until he turned 30. Since 2000, he worked as a consultant, analyst-programmer, engineer in industrial information systems, before getting involved in network science with a Ph.D. thesis (2009-12), 3 years after it was recognized by the US Research Council (2006). He has delivered numerous analyses, software codes, support services, reports, and research outcomes for various SMEs, large companies, French institutions, and territorial authorities – e.g., MAIF, Alstom Marine, Keolis, Bouygues Telecom, Bonduelle, APEC, or Rennes Métropole. As the main author of about 30 scientific publications since 2010, he still collaborates with the French “Conseil National pour la Recherche Scientifique” (CNRS) and to program committees/editorial boards for international conferences and journals (IEEE/ACM).
With more than 20 years of experience as a statistician and information system analyst, Emmanuel Keita is passionate about building bridges between expertise and data analysis, IQ, and AI, and therefore, about BayesiaLab! AI Associate Senior Consultant for Aveyo Consulting (Aveyo.fr), Emmanuel loves popularizing the advantages and fallacies of AI to a large audience (managers and general public) and also giving conferences and lectures to (future) data scientists. Committed to the societal issues of data science, Emmanuel is a National Defense Auditor (France, Prime Minister) and is currently involved in a private blockchain project (Digital seals, Avkee.com).
Steven F. Wilson, Ph.D., EcoLogic Research
Presented at the 8th Annual BayesiaLab Conference on October 26, 2020.
As global conservation actions become more urgent, informed decision-making requires robust analyses of the costs and benefits of policy options, based on available evidence. Recovery planning for endangered species must assume a cause-and-effect relationship between proposed management interventions and population responses; however, most current ecological knowledge is derived from observational studies because experiments are largely infeasible or unethical. Weak and conflicting inferences about causal mechanisms have created debate and confusion among decision-makers, planners and stakeholders. While causal modelling techniques are well-developed and common in other policy domains that face similar challenges, the approach is nearly absent in conservation biology. I examine the challenge of woodland caribou recovery efforts in Canada through the lens of causal modelling, highlighting recent, high-profile debates and illustrating how a causal modelling approach can help to bring resolution while supporting robust forecasting and decision support.
Steven F. Wilson, Ph.D., EcoLogic Research, 302-99 Chapel Street, Nanaimo, BC V9R 5H3, Canada, steven.wilson@ecologicresearch.ca
Steve Wilson has 30 years of experience working at technical and professional levels in strategic and operational planning for wildlife and other ecological values. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.
Using Bayesian Networks to Characterize Wildlife Habitat Use (Chicago, 2018)
The Social Graph—Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada (Paris, 2017)
Big data, small data: Bayesian networks in environmental policy analysis in Canada’s energy sector (Fairfax, 2015)
Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management (Nashville, 2016)
John Carriger, Ph.D., U.S. Environmental Protection Agency
Presented at the 8th Annual BayesiaLab Conference on October 27, 2020.
Coral reefs are highly valued ecosystems currently threatened by both local and global stressors. Given the importance of coral reef ecosystems, a Bayesian network approach can benefit an evaluation of threats to reef conditions by revealing details about the relationships between variables. To this end, we used available data to evaluate the overlap between local stressors (overfishing, watershed-based pollution, marine-based pollution, and coastal development threats), global stressors (acidification and thermal stress) and management effectiveness with indicators of coral reef health (live coral index, live coral cover, population bleaching, colony bleaching and recently killed corals). We constructed Bayesian networks using available data for each coral health indicator both globally and for specified regions (Pacific, Atlantic, Australia, Middle East, Indian Ocean, and Southeast Asia). Sensitivity analysis helped evaluate the strength of the relationships between different stressors and reef condition indicators. Management effectiveness was also examined for directionality and strength of relationships. The relationships between indicators and stressors were evaluated with conditional analyses of linear and nonlinear interactions. This process used standardized direct effects and target mean analyses to predict changes in the mean value of the reef indicator from individual changes to the distribution of the predictor variables. The standardized direct effects analysis identified higher potential risks between coral reef indicators and stressors in and across regions when relationships approximated linearity. Additional measures, including the minimums and maximums of the target mean analysis, were used to support the relationship analysis. The Bayesian network approach helped characterize relationships among indicators used for coral reef management by examining the sensitivity of reef condition indicators to indicators of threats and management effectiveness.
EPA Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
John F. Carriger (carriger.john@epa.gov), Susan H. Yee, William S. Fisher
U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation and Technology Division, Environmental Decision Analytics Branch
John Carriger is a research scientist at the U.S. Environmental Protection Agency’s Office of Research and Development in Cincinnati, Ohio. John has a marine science Ph.D. from the College of William and Mary. John’s research interests include applying risk assessment, decision analysis, and weight of evidence tools to environmental problems.
Dr. Lionel Jouffe, CEO, Bayesia S.A.S.
It has become customary for BayesiaLab to first present major new releases at the annual BayesiaLab Conference.
At the 8th Annual BayesiaLab Conference at the Laval Virtual World, Dr. Lionel Jouffe, CEO of Bayesia, provided a first glimpse of the new and improved features of BayesiaLab 10.
Presented at the 8th Annual BayesiaLab Conference on October 26, 2020.
Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.
Presented at the 8th Annual BayesiaLab Conference on October 27, 2020.
The use of marijuana, tobacco, and opioid analgesics has long been associated with psychological distress and chronic pain. The aim of the study was to identify the effects of internalizing and externalizing psychological symptoms utilizing Bayesian Network models to gain a greater understanding of the interrelationships that may exist between pain, psychological and sociodemographic variables. This study included adults enrolled in Wave 4 (2016-2017) of the Population Assessment of Tobacco and Health (PATH) Study. The PATH Study is an ongoing, longitudinal cohort study of tobacco use and related health outcomes in the U.S. Participants with data on variables related to cigarette smoking, marijuana use, psychological distress from the Global Appraisal of Individual Needs Short Screener (GAIN-SS), opioid use, and pain levels (n=32,551) comprised the study sample. The GAIN-SS identifies individuals at risk for mental health or substance use disorders and has been used across the 4-point Likert scale asking about past year problems with internalized disorder, externalized disorder, substance use, and crime/violent behaviors (crime/violent behavior items were excluded from the PATH Wave 4 study). Established smoking status was defined as lifetime use of 100 cigarettes whereas non-established use was defined as not reaching the 100 cigarette lifetime use threshold. The analyses included population sample weights which accounted for missing data. We employed an augmented naïve Bayes (ABN; Bayesialab 9) supervised learning algorithm to identify the interrelationships between the use of pain medications, smoking status, pain intensity, alcohol and marijuana use, GAIN-SS factors, and select sociodemographic characteristics. The initial Augmented Naïve Bayes was cross-validated via the K-folds procedure, with K=10. The final ABN structure was learned and optimized via the minimum description length (MDL) scoring algorithm. The initial MDL score was 732,140.862, representing Entropy (H) = 23.0785 (Standard Deviation: 3.9705), and the final MDL score was 689,686.468, representing Entropy (H) = 21.7285 (Standard Deviation: 3.9812), with mean information compression of 5.7427%. Overall relationship analyses with pain indicated that the internalized behavior factor was the most important predictive variable among these participants suggesting that by knowing pain, we reduce our uncertainty regarding internalized behavior by 2.49% on average. Smoking status, opioid use, and marijuana use were also found to be associated with pain. The severity of internalized, externalized problems and substance use disorders along with current and former established cigarette smoking were associated with high levels of pain intensity, suggesting that self-reported pain is an important factor to consider in smoking cessation and substance abuse counseling programs.
Mahathi Vojjala, MPH Doctoral Candidate, Department of Epidemiology, School of Global Public Health, New York University
Mahathi Vojjala is a third-year doctoral candidate in the Epidemiology track working with Dr. Raymond Niaura. She is a 2017 MPH graduate from New York University School of Global Public Health (GPH) with a concentration in Epidemiology. Prior to her MPH, Mahathi received a B.A. in religion and public health from Rutgers University. Mahathi’s previous research focused on youth smoking initiation and media advertising, dual and poly use of substances specifically marijuana and cigarettes among young adults, media portrayal of alcohol and tobacco in movie trailers and youth smoking rates, and more recently, use of oral analgesics combined with marijuana, alcohol, and cigarettes among people with chronic pain. Mahathi is primarily interested in assessing the benefits and risks of e-cigarettes by examining metabolic biomarker profiles of tobacco user groups using the Population Assessment of Tobacco and Health (PATH) Study.
Marcel de Dios, Ph.D. Assistant Professor, Department of Psychological Health & Learning Sciences, College of Education, University of Houston
Dr. Marcel de Dios is a faculty member in the Department of Psychological, Health and Learning Sciences (PHLS) at the University of Houston. He received his Ph.D. in Counseling Psychology from the University of Miami in 2007. He completed his clinical psychology predoctoral internship at Denver Health and Medical Center and moved on to a two-year postdoctoral research fellowship in behavioral medicine at Brown University Medical School. During his post-doctoral fellowship, Marcel conducted research related to smoking cessation with HIV + Latino smokers. Upon the completion of his fellowship in 2009, Marcel became a faculty member in the Department of Psychiatry & Human Behavior at Brown University Medical School. As a faculty member, his work expanded to include other sub-populations of substance users including young adult marijuana users, Latino light smokers, methadone maintenance smokers, and emerging adults struggling with alcohol and marijuana use. In October of 2012, Marcel relocated to Houston Texas, and became a faculty member in the Department of Health Disparities Research at MD Anderson Cancer Center where he continued his work in the area of smoking cessation funded through an NIH K01 award. In 2017, Marcel joined the faculty of the Counseling Psychology Ph.D. Program at the University of Houston. He has continued his work in the areas of substance use including projects related to young adult marijuana and alcohol users, smokers, and opioid abusers.
Helen Sanchez Doctoral Student, Counseling Psychology, College of Education, University of Houston
Helen Sanchez is a Counseling Psychology doctoral student at the University of Houston, working under the direction of Dr. Marcel de Dios. Prior to her doctoral studies, Helen completed research assistantships in the Health Behavior Research Group at Texas A&M University and the Prinsloo Neuromodulation Lab at MD Anderson Cancer Center. Currently, Helen is a graduate research assistant for the Psychology of Addiction Collaborative at the University of Houston, and her research interests broadly include substance use, health disparities among racial/ethnic minority populations, and health risk perception. Her current work focuses on the use of tobacco and other substances by South Asian Americans. Clinically, Helen works as a psychology intern with the Houston Fire Department, providing psychotherapy to first responders and their family members.
Raymond Niaura, Ph.D. Interim Chair of the Department of Epidemiology and Professor of Social and Behavioral Sciences, School of Global Public Health, New York University
Dr. Raymond Niaura is a psychologist and an expert on tobacco dependence and treatment, as well as substance use. Dr. Niaura has a long history of extramural funding for research projects that have examined the biobehavioral mechanisms of tobacco dependence, including factors that influence adolescent and early adult tobacco and e-cigarette use trajectories. He has also conducted a number of clinical trials that have focused on pharmacological and behavioral interventions for tobacco cessation with an emphasis on disadvantaged and vulnerable subpopulations. Dr. Niaura’s work has been highly influential, and he has published over 400 peer-reviewed articles, commentaries, and book chapters, including the book The Tobacco Dependence Treatment Handbook: A Guide to Best Practices.
His work over several decades has been highly influential and cited and has significantly shaped public policy related to tobacco use and cessation
Kurt S. Schulzke, JD, CPA, CFE, University of North Georgia
Presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
On May 7, 2021, Darkside hackers exploited a leaked Colonial Pipeline Corporation (CPC) password, breaching a dormant VPN to infiltrate CPC’s IT system. Lacking a contingency plan, CPC entirely shuttered its pipelines, which at the time carried 45 percent of all jet fuel and gasoline consumed on the East Coast of the United States. This ransomware hack showcased stereotypical weaknesses in cybersecurity modeling, controls, and compliance monitoring and revealed the company's failure to create a response playbook or contingency plan, as required by U.S. Department of Transportation regulations. This presentation illustrates the use of Bayesian networks and influence diagrams for cybersecurity risk modeling, assessment, ranking, and management and suggests how their use might have prevented the Colonial Pipeline hack and/or mitigated its consequences to the company and other stakeholders.
Kurt Schulzke, JD, CPA, CFE, is a Professor of Accounting & Law at the University of North Georgia. His teaching, research, and consulting thrive at the intersection of data science, accounting, law, and risk management. He has published in the Columbia Journal of Transnational Law, Vanderbilt Journal of Transnational Law, Tennessee Journal of Business Law, Journal of Forensic Accounting Research, and The Value Examiner. MAcc (Brigham Young University), J.D. (Georgia State University), M.S. Applied Statistics (Kennesaw State University).
Steven F. Wilson, Ph.D., EcoLogic Research
Presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
Environmental policymaking is challenging because systems are complex, and rarely can we conduct experiments to test the relative costs and benefits of different policy options. Causal analysis methods allow us to estimate causal effects from observational data, and such methods are being applied increasingly often to predict the relative benefits of alternative policies. However, predictions based on only average causal effects provide an incomplete assessment of the value of potential interventions. Decision-makers also need to know how likely an outcome is to occur without the intervention (i.e., an assessment of causal attribution) or what outcomes could be expected if the intervention was only selectively applied (i.e., estimating context-specific causal effects). Answering these questions requires applying the counterfactual reasoning of “rung 3” of Pearl’s causal hierarchy. In fact, Pearl argued explicitly in his book Causality that “policy analysis is an exercise in counterfactual reasoning.” I used Bayesian Networks to model counterfactual outcomes on caribou populations of different land use policy interventions. While there are theoretical limitations to using Bayesian Networks for this purpose, the resulting counterfactual insights still provide additional value to decision-makers compared to observational or interventional analyses.
Steven F. Wilson, Ph.D., EcoLogic Research, 302-99 Chapel Street, Nanaimo, BC V9R 5H3, Canada, steven.wilson@ecologicresearch.ca
Steve Wilson has 30 years of experience working at technical and professional levels in strategic and operational planning for wildlife and other ecological values. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.
The Social Graph—Using Bayesian Networks to Identify Spatial Population Structuring Among Caribou in Subarctic Canada (Paris, 2017)
Use of causal modeling with Bayesian networks to inform policy options for sustainable resource management (Nashville, 2016)
Big data, small data: Bayesian networks in environmental policy analysis in Canada’s energy sector (Fairfax, 2015)
2023 BayesiaLab Conference
March 23-24, 2023
10th Annual BayesiaLab Conference
October 24-28, 2023
9th Annual BayesiaLab Conference
October 11-15, 2021
8th Annual BayesiaLab Conference
October 26-30, 2020, at the Laval Virtual World
7th Annual BayesiaLab Conference
October 10-11, 2019, at the
North Carolina Biotechnology Center
6th Annual BayesiaLab Conference
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in Chicago, Illinois
A general admission conference ticket is $399 (plus fees).
Participants from academic institutions, NGOs, government agencies, and the military are eligible for a discounted conference ticket rate of $299 (plus fees).
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You can also call the hotel directly to make your reservation: +1 513-487-3800.
Introductory BayesiaLab Course
April 8-10, 2024
Advanced BayesiaLab Course
April 15-17, 2024
2023 BayesiaLab Conference
March 23-24, 2023
A Zoom Virtual Event
10th Annual BayesiaLab Conference
October 24-28, 2023
A Zoom Virtual Event
9th Annual BayesiaLab Conference
October 11-15, 2021
A Zoom Virtual Event
8th Annual BayesiaLab Conference
October 26-30, 2020
A Virtual Reality Event
7th Annual BayesiaLab Conference
October 10-11, 2019
North Carolina Biotechnology Center
6th Annual BayesiaLab Conference
November 1-2, 2018
Chicago, Illinois
Bayesian Network Analysis of Cigarette Smoking and E-cigarette Use in U.S. Population Samples
Applying Bayesian Media Influence Network Maps to Women’s Apparel
Modeling Meta's response to the Irish DPC's sanctions for GDPR violations
Decision-Making in the Era of ChatGPT: Thoughts Inspired by the I Ching (易经)
Collaborative Alpha: Mixing Human and Machine Intelligence to Achieve Optimal Outcomes
Airline Catering Supply Chain Performance During Pandemic Disruption: A Bayesian Network Approach
Conceptual Bayesian Networks for Groundwater Remediation and Assessment
Market Share and Metrics Association: An Insight into Real-World Data Using Bayesian Networks
Market Share and Metrics Association: An Insight into Real-World Data Using Bayesian Networks
Predicting Stress Corrosion Cracking: A Bayesian Network Approach for Duplex Stainless Steels
International Migration and Weather Disasters: Econometric Modeling vs. Bayesian Network Analysis
Bayesian Network Analysis of Cigarette Smoking and E-cigarette Use in U.S. Population Samples
Ray Niaura New York University
Lionel Jouffe Bayesia S.A.S.
Hellixia & Generative AI: Creating Semantic and Causal Bayesian Networks for Decision Support Video Available
Kurt Schulzke University of North Georgia
Assessing & Optimizing Cyber Risk with Bayesian Networks: The Colonial Pipeline Case Video Available
Martin Block Northwestern University
Kurt Schulzke University of North Georgia
Emmanuel Keita Sundiata
Vikram Suresh University of Cincinnati
Unifying Frameworks for Reward-Aversion Judgment: A Bayesian Analysis Video Available
Hana C. Long NC State University
Predicting PFAS Exposure Risks from Rural Private Wells Video Available
Gabriel Andraos Voya Financial
Steven F. Wilson EcoLogic Research
Counterfactual Reasoning Using Bayesian Networks for Environmental Policy Analysis Video Available
Fathien Azuien Yusriza Airbus Malaysia
John Carriger U.S. EPA
Anand Wilson Course5 Intelligence
Rahul Pandey Course5 Intelligence
Yong Zhang Procter & Gamble
Leverage GenAI and Causal Inference to Disrupt Innovation Video Available
Abraham Rojas Zuniga Curtin University
Edwin Hui University of St. Andrews
An Application of Dynamic Bayesian Networks to Model Regime Shifts and Changepoint Processes Video Available
Alexander Alexeev Indiana University
Steven Frazier Georgia Pacific
Transforming Paper Product Quality and Machine Performance with Machine Learning & Bayesian Networks Video Available
Shu Xu New York University
John Carriger, Ph.D., U.S. Environmental Protection Agency
To be presented at the 2024 BayesiaLab Spring Conference in Cincinnati on April 12, 2024.
Causal structural models are used to capture knowledge of a problem domain through framing potential events as random variables and connections as causal arcs. Moreover, the properties of causal structural models foster additional insights on causal and inferential interactions among variables from interventions and observations. Conceptual site models are commonly used in environmental assessments for capturing the knowledge of the fate, transport, and risks at contaminated sites and form the basis for simulation models. The usage of causal structural models with conceptual site modeling may provide additional value for site remediation and assessments. We call this combination conceptual Bayesian networks (CBNs) and explore their application potential in contaminated site management for assessing the subsurface movement of contaminated plumes. Once constructed, the CBN can capture the hypothesized locations and movements of a plume as well as critical zones of offsite flux. Causal pathway identification can examine offsite transport pathways and the potential effects of remediation decisions that intervene on those pathways. Interventions for containing or removing subsurface contamination and breaking the transport pathways are graphically represented as decision nodes. Finally, measurement node types can explicitly include lines of evidence for subsurface processes in the CBN. Acausal pathways from influence paths provide additional information on statistical inferences when lines of evidence are observed individually or in conjunction. The CBN concept may provide additional insights beyond traditional conceptual site models and could be a valuable component in a site manager’s toolbox.
The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
John F. Carriger, Michael C. Brooks, Carolyn Acheson, Ronald Herrmann, Lee Rhea
US Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation and Technology Division, Cincinnati, OH
US Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Groundwater Characterization and Remediation Division, Ada, OK
US Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation and Technology Division, Cincinnati, OH (retired)
John Carriger is a research scientist at the U.S. Environmental Protection Agency’s Office of Research and Development in Cincinnati, Ohio. John has a marine science Ph.D. from the College of William and Mary. John’s research interests include applying risk assessment, decision analysis, and weight of evidence tools to environmental problems.
Register here for the 2024 BayesiaLab Spring Conference, April 11-12, 2024:
Edwin Hui, University of St Andrews
Understanding the dynamics that regulate ecological resilience is becoming increasingly important in today’s world, as ecosystems face multiple global, regional, and local pressures. If pressures exceed a threshold, this may trigger a regime shift, where a system undergoes a step change to another state that can last for substantial periods of time. However, modeling such change is not simple as ecological data is scarce, and models often assume that relationships within ecosystems remain homogenous over time. In this talk, we document the application of non-homogenous Dynamic Bayesian Networks to various complex systems known to have undergone major structural changes.
Edwin Hui is a Ph.D. student from the University of St Andrews, where his research focuses on developing computational models to study resilience and regime shifts across complex systems. He is interested in applying a variety of statistical and computational tools to address ecological questions and study complex systems theory. Throughout his Ph.D., he aims to develop novel computational approaches to study complex systems across different disciplines, ranging from ecological to macroeconomic systems.
Presented at the in Cincinnati on April 12, 2024.
Serdar Semih Coşkun, İstanbul University Faculty of Economics
Presented at the 2023 BayesiaLab Spring Conference.
Football (soccer) stocks are substantially subject to investor sentiment stemming from football fields. Evaluating sentiment functions helps us understand how investors interpret field signals and attach value to those signals in stock markets. This study develops the Gaussian investor sentiment process exploration programming (GISPEP) framework for exploring investor sentiment as a function of probabilistic field signals. The GISPEP provides an alternative event-study approach based on prospect theory and Bayesian analysis. We use the GISPEP to set the causality between match results and stock returns of the Fenerbahçe (FB), Galatasaray (GS), and Beşiktaş (BJK) football clubs in Turkey. A natural experiment also enables us to test the effect of competitive emotion that varies across two seasons. Our results indicate that competitive emotions regulate the asymmetric rise of availability and loss aversion heuristics under ambiguous field signals. In addition, loss signals increase the heterogeneity of market expectations.
Serdar Semih Coşkun is an assistant professor at İstanbul University Faculty of Economics, Turkey. Serdar holds a Ph.D. in Business Administration from the same university. Serdar’s research interests include Bayesian statistics, behavioral economics, and supply chain management.
Jenny Betsabé Vázquez-Aguirre, University of Veracruz
Presented at the 2023 BayesiaLab Spring Conference.
The study of causality can be traced back 300 years; its origins are attributed to two thinkers of the epoch (Emmanuel Kant and David Hume). Both philosophical currents are opposed, though both have the same objective, to explain the origins of causal processes in the human brain.
Several disciplines have been studying causality from different prospects; two of these disciplines are Cognitive Psychology (CP) and Artificial Intelligence (AI). Despite these areas having their origins together, currently, they work separately.
There are multiple efforts to replicate and explain causality based on prior knowledge; from CP, there have been different proposals; its aim is to describe how to learn the natural cause-effect relationships, and from AI, there are multiple algorithms focused on estimating or predicting causality.
From IA, a method for learning causality is through the intervention of variables in Bayesian Networks (BN); however, this requires prior knowledge that indicates the cause-effect direction within the connections in the network. For the above, our proposal is a joint implementation to create Causal Bayesian Networks (CBN) from the fusion of two areas, the CP and IA. The principal goal is an integrated two algorithms for the construction of the graphical models (CBN) from the dataset that can learn the causal relationships, giving direction to the arcs within the network, such that indicate who is the cause and who is the effect. For the construction, we used the Rescorla-Wagner model in the first algorithm and the Power-PC theory for the second; both methods belong to the CP.
The results obtained with this approach have been encouraging, and the CBNs acquired can be used to intervene in variables and estimate causal probabilities. For comparison, we used traditional Bayesian Networks proposed by AI.
We have tested with real and repository datasets, comparing our networks with those drawn manually by the experts who have provided us with the data. In the beginning, only 14% of the BNs constructed with traditional AI algorithms could be used for intervention purposes. Most of the CBNs obtained with this proposed algorithm can be used for this purpose and reflect more cause-effect connections than those created with other AI algorithms.
Jenny Betsabé Vázquez-Aguirre was born in Veracruz, Mexico. She has a bachelor's degree in statistics and two master's degrees: one in Quality Management and the other in Artificial Intelligence.
She has worked as a data analyst, software tester, process manager, as well as statistical methodology teacher.
She is currently studying the last semester of a Ph.D. in Artificial Intelligence, in which she is working on causal analysis through Causal Bayesian Networks principally, but also, she is continually working on data analysis with machine learning.
A Zoom Virtual Event — October 24–28, 2022
2022 marked a significant milestone for Bayesia. We hosted the 10th Annual BayesiaLab Conference. This special event featured more speakers from more fields than ever! Our exciting lineup of talks reflected the ever-growing relevance of Bayesian networks for research, analytics, and reasoning.
If you missed some of the talks, we uploaded recordings of all presentations and the corresponding slides.
Alexandra Chirilov, James Pitcher, and Andrzej Surma, GfK
In this second presentation, the authors used real survey data from a syndicated study covering two categories, three countries, and more than 60 brands to assess the differences between the two methods at any point in time (cross-section view) and over time (longitudinal view).
Please also see the first presentation by GfK at the BayesiaLab Conference:
Alexandra Chirilov is leading GfK’s Global Product Development Practice for consumer and brand intelligence. Her insights and research have been featured in publications such as Esomar, Journal of Marketing Research, Sawtooth, and more. She is a winner of the ESOMAR Corporate Young Professional Award, among other industry awards.
James Pitcher leads GfK’s Marketing Sciences team in the UK, which designs and delivers sophisticated analytical solutions to solve client high-value problems. He has spent the last 15 years providing statistical advice and consultancy within the market research industry, working with clients across many different sectors and regions. James is an expert in conjoint analysis, brand research, pricing, consumer segmentation, and a wide range of multivariate techniques, including Bayesian Networks analysis, contributing to the development of innovative techniques and regularly presenting at international conferences.
Andrzej Surma works as a methodological lead for the Global Product Development Team. He has more than ten years of experience in data analysis. With a background in mathematics, Andrzej loves to solve problems, such as recognizing the Greek letters contained within formulas that describe mathematical models! Recently, he co-created a Bayesian Networks approach to running Key Drivers Analysis on brand tracking data. Spatial data analysis is another particular interest of his. Andrzej likes to be active in his free time, playing football and riding bikes and is inspired daily by his wife and their three children.
Presented at the on Wednesday, October 26, 2022.
Mohammad Reza Valaei & Vahid Khodakarami, Ph.D., Bu-Ali-Sina University, Iran
Presented at the 2023 BayesiaLab Spring Conference.
Assessing risk for a start-up is always a complex and challenging task, as historical data is typically unavailable. Traditional methods are inadequate in capturing the full complexity, so more sophisticated tools are required. This paper presents a method for estimating the default rate at various stages of a start-up's life cycle using an expert-elicited Bayesian Networks (BNs) methodology. A prototype BN model is proposed to combine diverse sources of information, including historical data and expert knowledge. The model has a hierarchical structure to capture start-ups' known risk factors. It also uses the Noisy-OR operator to capture the unknown risks in each of the main categories.
The prototype model can be adjusted to capture the unique characteristics of each start-up and investor. 3 case studies were used to demonstrate the applicability of the model. The proposed method reduces the cognitive error of experts, takes advantage of the learning feature of BNs when updating default estimations, and takes into account the impact of investors' risk appetite. It also allows for ranking the most effective risk factors at various stages of the start-up life cycle.
Mohammad Reza Valaei, Bu-Ali-Sina University, Hamedan, Iran
Mohammad Reza Valaei is a Ph.D. candidate in Industrial Engineering at Bu-Ali-Sina University. His research interests include practical studies in financial risk management, start-up valuation, venture capital, and portfolio optimization.
Vahid Khodakarami, Ph.D., Bu-Ali-Sina University, Hamedan, Iran
Vahid Khodakarami is an associate professor at Industrial Engineering Department at Bu-Ali-Sina University. During his Ph.D. study at Queen Mary University of London in 2004, he was introduced to the Bayesian Networks technology. Since then, applying BN in real-life projects has become his main research interest. He has published several papers. Risk Assessment, Reliability, Sustainability Engineering, and Project Management are among Vahid's other research interests.
Yann Corriou, Charier S.A.S.
Presented at the 10th Annual BayesiaLab Conference on Tuesday, October 25, 2022.
In the public works environment, avoiding breakdowns of construction machines is a major challenge. Indeed, this phenomenon can represent a significant economic cost at three different levels. First, we need to pay for the repair of the machine, which is called the direct cost. Then, a breakdown will eventually lead to a delay in the progress of the work or to the need to rent another machine to replace the unavailable one, all of this representing the indirect cost. And finally, a breakdown can also affect the lifetime of a machine, and optimizing this lifetime is a priority when handling a fleet of public works equipment.
To reach our goal, we are developing different Bayesian networks using an expert-driven approach with BEKEE. The presentation will be about the development of our networks, how we use this process to target useful data for our CMMS, and how we plan to include the result of the networks in our CMMS.
Yann Corriou, Data Scientist, Charier S.A.S. yann.corriou@charier.fr
I studied for five years (2015-2020) at INSA Rennes (engineering school) in the Department of Applied Mathematics. After an internship (2019) and a one-year work-study contract (2019-2020), I am now working full-time as a data scientist at CHARIER, a public works company operating mainly in the West of France.
We present a digital humanities application of Bayesian networks to investigate changes in science fiction portrayals of exoplanets (planets outside our solar system) since the 1990s discovery by astronomers of real exoplanets. Bayesian network analysis is applied to a representative database of fictional exoplanets to determine if the publication date influences fictional exoplanet characteristics. Networks are generated using Banjo, search methodology decisions are confirmed with BayesPiles analysis, and the results are visualised using GraphViz. Results show fictional exoplanets from media created after the discovery of real exoplanets are moderately less likely to host established human populations and slightly less likely to host intelligent native life. This change in response to the scientific discovery of thousands of exoplanets, many of which are not hospitable to humans, provides genre-wide evidence indicating that science fiction does communicate rapidly evolving scientific results. This research demonstrates the potential for Bayesian network analysis as a promising data science methodology in interdisciplinary academic practice.
Emma Johanna Puranen, University of St Andrews (Presenter)
V Anne Smith, University of St Andrews
Emily Finer, University of St Andrews
Christiane Helling, The Space Research Institute (Institut für Weltraumforschung, IWF)
Presented at the on Thursday, October 27, 2022.
Emma Johanna Puranen is a St Leonards’ Interdisciplinary Doctoral Scholar at the University of St Andrews, combining astronomy, data science, and media studies in her research on exoplanets in science fiction. A member of the St Andrews Centre for Exoplanet Science, she is very interested in questions of astrobiology and space ethics, including how humans portray speculative space travel in science fiction. Learn more at her website .
Presented at the 9th Annual BayesiaLab Conference on October 14, 2021.
Mountain goats are an iconic wildlife species of western North America, inhabiting steep and largely inaccessible terrain in remote areas. But they are also at risk from genetic isolation, climate change, and a variety of other stressors. Managing populations is challenging because mountain goats are difficult and expensive to inventory, and biologists have to rely on models to predict the species’ abundance and distribution. I used landscape characteristics evident at point locations of mountain goat observations, along with an equal number of random locations, to learn the structure and parameters of a Bayesian network that predicted the suitability of habitats for mountain goats. I then used the model to process evidence scenario files of >100 million records to map the suitability of mountain goat habitat at a 25-m resolution throughout the study area. The model has subsequently been used to assess the effectiveness of current protected areas for mountain goats and to generate preliminary population estimates. Modeling the system as a Bayesian network provided a number of advantages over traditional parametric approaches because, as with many ecological studies, input variables were correlated, and animals exhibited non-linear responses to landscape conditions.
Steven F. Wilson, Ph.D. EcoLogic Research 302-99 Chapel Street Nanaimo, BC V9R 5H3 Canada steven.wilson@ecologicresearch.ca
Steve Wilson has 30 years of experience working at technical and professional levels in strategic and operational planning for wildlife and other ecological values. He specializes in quantitative approaches to decision support and policy analysis. Steve holds a Ph.D. in wildlife ecology from the University of British Columbia in Vancouver.
October 26–30, 2020 — A Virtual Reality Event
Our first all-virtual BayesiaLab Conference at the Laval Virtual World was a great success! With nearly 200 registrations, we had more participants than ever. Despite time differences, researchers from British Columbia to Japan joined us in real time for the event. A big "thank you" to all who made this conference a success!
If you missed some of the talks, we uploaded recordings of all presentations and the corresponding slides.
Presented at the 10th Annual BayesiaLab Conference on Friday, October 28, 2022.
The differential diagnosis of respiratory diseases is usually a challenge for medical specialists in the first line of care, which has increased under the current COVID-19 pandemic. A Clinical Decision Support System — CDSS — is being developed using Bayesian Networks – BNs – to help physicians diagnose respiratory diseases, including those related to COVID-19. Network structure has been elicited from expert physicians, and network parameters (disease prevalence, symptoms, findings, and lab results conditional probabilities) were extracted from relevant bibliography or currently standard global information sources. The CDSS is being tested using case studies taken from real situations, provided and validated by physicians. The resulting system demonstrates the suitability and flexibility of BNs for diagnosis support.
Dr. Ernesto Ocampo, Ph.D., is a senior full-time professor at the Catholic University of Uruguay Computer Science Department, where he teaches subjects such as Artificial Intelligence, Machine Learning, and Algorithms. His research focus is on Artificial Intelligence applied to Clinical Decision Support Systems (e.g., Acute Bacterial Meningitis, HIV/AIDS, respiratory diseases, and cancer).
His background is in software engineering and holds a Ph.D. in Computer Science from the University of Alcalá, Spain.
An IEEE Senior Member, Dr. Ocampo has worked in the software industry for more than 30 years, currently as a technical consultant for Qualisys Software and Technologies - www.qualisyss.com-).
Dr. Silvia Herrera, MD, is a senior pediatric physician with more than 30 years of professional experience. Dr. Herrera worked for 25 years as an internal pediatrician in the Central Armed Forces Hospital of Uruguay, and for several years she was part of the pro-bono health team that focused on children with HIV/AIDS at the National Pediatric Centre of Reference.
She currently works in a pre-hospital pediatric emergency unit and the pediatric emergency room of a private health provider hospital.
Dr. Herrera has helped the UCU Computer Science Department for several years as a field expert in various CDSS research projects.
Juan Francisco Kurucz is an Informatics Engineer who graduated from the Catholic University of Uruguay, where he works as an assistant professor in Artificial Intelligence and other computer science courses. He is an active academic researcher on Artificial Intelligence and is currently focused on the application of Bayesian Networks and Deep Learning to Clinical Decision Support.
In the professional field, Juan Francisco works as a Machine Learning Engineer at an AI Software Company —Tryolabs — where he specializes in Computer Vision. He also participates in the IEEE as a volunteer member.
Lucas Lois is a Software Engineer who graduated from the Catholic University of Uruguay, where he works as an assistant professor in Computer Science courses. His research area is Artificial Intelligence in Health, focused on the application of Natural Language Processing to Named Entities Recognition in Electronic Health Records and Bayesian Networks applied to Clinical Decision Support.
In the professional field, Lucas also has several years of software development experience, working currently as a software team leader at December Labs, a high-touch boutique Design & Development shop.
Cross-examination is a method for testing the evidence presented at trial by asking probing questions. It is an integral part of the right to confront one’s accusers, as enshrined in the constitutions of many countries. But there is little scholarly work that analyzes cross-examination, its scope, and its function at trial. Until we know what cross-examination consists in, the substance of the fundamental right to confrontation remains elusive. This talk makes a first attempt at clearing the ground by articulating an analysis of cross-examination using Bayesian networks. This conceptual ground clearing will provide a framework to identify when cross-examination may go wrong and hinder the search for an accurate determination of the facts.
Presented at the on Thursday, October 27, 2022.
Marcello Di Bello is an Assistant Professor of Philosophy at Arizona State University. He holds a Ph.D. in Philosophy from Stanford University and an MSc in Logic from the University of Amsterdam. His research interests include evidence and probability, risk and decision-making, statistics in the law, and algorithmic fairness. He is currently working on a book with Rafal Urbaniak of Gdansk University on "legal probabilism," which you can learn more about here: .
Raphael Girod, MAHA
The immunization approach, Expanded Programme on Immunization (EPI), is a powerful public health strategy for improving child survival; the policy for any Ministry of Health is to ensure that as many children as possible receive the full series of vaccines on their national routine immunization schedule.
The overarching goal of that study is to demonstrate (i) the usefulness of the data mining and modeling approaches in the context of an immunization program and, for the communication aspect, (ii) how mapping and analytics capabilities could be used so as to brainstorm and communicate the proposed actionable insights among the decision-makers in mandated coordination bodies at national and regional levels.
The EPI Bayesian Network Model for Ghana displays the available variables (data elements extracted from the District Health Information & Management System) and the arcs, which have been “manually” laid down, thanks to the theory of change, to justify causal assumptions (or the linkages among the variables in the EPI model). Indeed, Public health knowledge is key.
Out of these data elements, the number of children vaccinated and the number for the three types of vaccination sessions (fixed, outreach, and at school) are considered specifically.
Thanks to the optimization algorithm, it is possible to lay down that the best solution gears towards increasing the number of fixed and school vaccinations sessions and, lowering the number of outreach vaccinations sessions, but because of the several contextual factors to be considered, any realistic and meaningful concrete decisions should be taken only at district or sub-district levels.
Whatever the diversity and the complexity of the local situation at the sub-district level, we take recourse of the “batch outlier” procedure so as to come up with priority actions.
A Bayesian network can serve as an inference engine, and thus simulate that public health program comprehensively. Through simulation, we can obtain all associations that exist in the EPI program, and, most importantly, we can compute causal effects directly. Overall, strategies to improve vaccination must be percolated top-down up to sub-districts and communities.
Raphael is a project manager by experience, he gained public health skills as (i) health expert in charge of many result-oriented monitoring missions in Asia and Africa, (ii) as health project coordinator based at the Ministry of Health in Guinea, and (iii) as Local Fund Agent Project leader for Global Fund in Burkina-Faso, all these complementary experiences enables him to fully appraise the strategic, financial, epidemiological and impact evaluation stakes in the process of providing reliable information for high-quality programming…The most updated capability relates to modeling and data-mining, thanks to BayesiaLab.
Passionate about data analysis and public health, his current work aims at collating data sources in order to structure complex datasets, to inform indicator measurements in order to support the strengthening of knowledge management related to global health, especially in regards to immunization programs.
Presented at the on October 26, 2020.
Raphael Girod is the founder of the MAHA organization. MAHA stands for .
Presented at the 9th Annual BayesiaLab Conference on October 12, 2021.
Cracking is one of the major factors which leads to the deterioration of road structures. Globally, United Arab Emirates is a country with high-standard road networks. Highway organizations take the initiative to measure the cracking and confirm whether it is within the prescribed limit. However, such monitoring activities are expensive in terms of cost, labor, and machinery, which ultimately leads to failure in the timely repair and maintenance activities of the roads. This results in a reduction of the service life of the pavements. This study aims to develop a solution for this problem by studying the historical data of factors that influence cracking in roads. To perform this, data related to major road networks in the country are collected from the highway agency. The data include environmental factors, traffic intensity, and factors like road type and age of the road. A Supervised Learning algorithm will determine the role of each factor that contributes to cracking. Once the significance of each factor is analyzed, further analysis based on a dynamic Bayesian network will aid in estimating the future values of cracking on the roads without measuring it. This study thus can be a major contribution in the transportation field to improve the quality of road networks.
Ms. Babitha did her B.Tech in Civil Engineering and M.Tech in Construction Engineering and Management from India. She has worked as a Research Assistant at UAE University in the Civil and Environmental Engineering Department, and her work was mainly focused on applying Artificial Intelligence techniques in the Civil Engineering field. Currently, she is doing a Ph.D. at UAE University on a topic involving the application of Bayesian networks.
Alta de Waal, Ph.D., University of Pretoria
Presented at the 8th Annual BayesiaLab Conference on October 27, 2020.
While activity-based travel demand generation has improved over the last few decades, the behavioural richness and intuitive interpretation remain challenging. We argue that it is essential to understand why people travel the way they do and not only be able to predict the overall activity patterns accurately. If one cannot understand the ‘why?” then a model's ability to evaluate the impact of future interventions is severely diminished. Bayesian networks (BNs) provide the ability to investigate causality and is showing value in recent literature to generate synthetic populations. This research is novel in extending the application of BNs to daily activity tours. Results show that BNs can synthesise both activity and trip chain structures accurately. It outperforms a frequentist approach and can cater for infrequently observed activity patterns, and patterns unobserved in small sample data. It can also account for temporal variables like activity duration.
Alta de Waal, Ph.D. Centre for Artificial Intelligence Research Department of Statistics, Faculty of Natural and Agricultural Sciences University of Pretoria, South Africa
Alta currently holds a senior lecturer position in the Department of Statistics, University of Pretoria, South Africa. She has 20 years of experience in the design, development, and implementation of different components in the AI value chain. She develops Bayesian network models in application areas such as student throughput models, wildlife security, environmental risk management, and transportation. Alta also studies natural language processing (NLP) with a special interest in probabilistic distributional semantic methods.
Spatially Discrete Probability Maps for Anti-Poaching Efforts (Paris, 2017)
Steven Frazier, Georgia Pacific
Presented at the 8th Annual BayesiaLab Conference on October 27, 2020.
Contradictory conclusions have been made about whether unarmed blacks are more likely to be shot by police than unarmed whites using the same data. The problem is that by relying only on data from ‘police encounters,’ there is the possibility that genuine bias can be hidden. We provide a causal Bayesian network model to explain this bias – which is called collider bias or Berkson’s paradox – and show how different conclusions arise from the same model and data. We also show that causal Bayesian networks provide the ideal formalism for considering alternative hypotheses and explanations of bias.
Steven Frazier is an experienced engineer with over 40 years of experience in manufacturing management and operations. Graduating with a BSME from Washington University St. Louis in 1979, he has managed and worked in production operations, maintenance, environmental, and safety for Procter and Gamble, ITW, Coca-Cola, Georgia Pacific, and now with OnPoint, a part of Koch Engineered Solutions. As such, he has been involved in the manufacture of a variety of products, from Cascade dishwashing detergent, flexible plastic, and stretch films, metalized films, paper, and building products such as lumber, plywood, oriented strand board, and gypsum board. In 2000, he received 3 patents related to liquid packaging and bag-in-box technology while working for Coca-Cola. He became interested in Bayesian Networks in 2014 because of their ability to predict, discover, and define cause and effect displayed in simplified graphical models. With his extensive domain knowledge, he is currently working as an advanced analytics process engineer for OnPoint, applying machine learning and Bayesian networks to help customers optimize and solve problems related to their manufacturing operations.
Steven Frazier can be described as a “geek’s geek” – throughout his career, he has been the go-to guy in learning increasingly complex applications to enhance the tool kit for production managers, for operations excellence, problem-solving analytics, and innovations to increase safety, optimize production and bottom-line profitability. He has 3 United States patents; he is a peer-reviewed co-author, and his breadth of experience spans iconic brands of Anheuser Busch, Procter and Gamble, Coca-Cola, and Georgia Pacific. With a Washington University Bachelor of Science in Mechanical Engineering, he has managed and worked in production operations, maintenance, environmental, mergers and acquisitions. As a comparative advantage, Steve has leveraged Bayesian Networks -- to increase manufacturing quality performance while maximizing profit, to examine beneficial reuse analysis, and to assess competitive product quality as an element of capturing increased market share.
In his role with Georgia Pacific, he built a first-of-its-kind materials model that surpassed all other comers – both “foreign & domestic.” Today he is part of the Koch Industries OnPoint working as an advanced analytics process engineer applying machine learning to optimize customers manufacturing operations. He became a “thought leader” for Bayesialab when he gave his wife a crash course in big data Bayesian Networks to help her complete her Masters in Sustainability at Georgia Tech. Now that is real-world brand reach – Bayesian Networks applied.