Opening Remarks

Ofer Harel, University of Connecticut

Ofer Harel Ofer Harel, Ph.D. is the Interim Dean of the College of Liberal Arts and Sciences and a professor in the Department of Statistics. Dr. Harel was the Associate Dean for Research and Graduate Affairs in the College of Liberal Arts and Sciences 2021-2023; the Director of Graduate Admissions at the Department of Statistics 2016-2021 and was a principal Investigator in the Institute for Collaboration on Health, Intervention, and Policy (InCHIP) at the University of Connecticut 2010-2016. Dr. Harel received his doctorate in statistics in 2003 from the Department of Statistics at the Pennsylvania State University; where he developed his methodological expertise in the areas of missing data techniques, diagnostic tests, longitudinal studies, Bayesian methods, sampling techniques, mixture models, latent class analysis, and statistical consulting. Dr. Harel received his post-doctoral training at the University of Washington, Department of Biostatistics, where he worked for the Health Services Research & Development (HSR&D) Center of Excellence, VA Puget Sound Healthcare System, and the National Alzheimer’s Coordinating Center (NACC). Dr. Harel has served as a biostatistical consultant nationally and internationally since 1997. Through his collaborative consulting, Dr. Harel has been involved with a variety of research fields including, but not limited to Alzheimer’s, diabetes, cancer, nutrition, HIV/AIDS, health disparities, anti-racism, and alcohol and drug abuse prevention. Dr. Harel is a member of the National Academy of Science, Engineering and Medicine’s Committee on Applied and Theoretical Statistics. Dr. Harel was a member of the (now restructured) Biostatistical Methods and Research Design (BMRD) Study Section at the National Institute of Health and was appointed to the Bureau of Labor Statistics Technical Advisory Committee (BLSTAC) at the U.S. Bureau of Labor Statistics among many national elected and appointed positions.

Eric Kolaczyk, McGill University

Eric Kolaczyk Eric Kolaczyk is a professor in the Department of Mathematics and Statistics, and inaugural director of the McGill Computational and Data Systems Initiative (CDSI). He is also an associate academic member of Mila, the Quebec AI Institute. His research is focused at the point of convergence where statistical and machine learning theory and methods support human endeavors enabled by computing and engineered systems, frequently from a network-based perspective of systems science. He collaborates regularly on problems in computational biology, computational neuroscience and, most recently, AI-assisted chemistry and materials science. He has published over 100 articles, including several books on the topic of network analysis. As an associate editor, he has served on the boards of JASA and JRSS-B in statistics, IEEE IP and TNSE in engineering, and SIMODS in mathematics. He formerly served as co-chair of the US National Academies of Sciences, Medicine, and Engineering Roundtable on Data Science Education. He is an elected fellow of the AAAS, ASA, and IMS, an elected senior member of the IEEE, and an elected member of the ISI.

Joan Buenconsejo, BMS

Dr. Joan Buenconsejo Dr. Joan Buenconsejo is the Vice President and head of Cardiovascular and Neuroscience Biostatistics at Bristol-Myers Squibb. In addition to leading a team of talented statisticians who support the development of innovative therapies for patients with CV and neurological diseases, she is also co-leading a BMS-wide workstream that aims to foster trial design innovation and optimization. Dr. Buenconsejo has over 20 years of drug development experience, having worked at the Food and Drug Administration and AstraZeneca before joining BMS. While she calls statistics as her “bread and butter” skillset, she is also passionate about applying novel tools and technology to generate insights, simplify workflows, and reduce patient burden in clinical research. She is also an advocate of using innovative trial designs to accelerate the development of promising new therapies. Dr. Buenconsejo holds a Ph.D. and an MPH in Biostatistics from Yale University.

Dr. Kun Chen, University of Connecticut

Dr. Kun Chen Kun Chen is a Professor in the Department of Statistics at the University of Connecticut (UConn) and a Research Fellow at the Center for Population Health, UConn Health Center. He has been a Fellow of the American Statistical Association (ASA) since 2022 and an Elected Member of the International Statistical Institute (ISI) since 2016. His research mainly focuses on large-scale multivariate statistical learning, statistical machine learning, and healthcare analytics. He has extensive interdisciplinary research experience in several fields, including ecology, biology, agriculture, and population health. Dr. Chen has graduated with over ten PhDs and received Recognition for Teaching Excellence at UConn multiple times. He has also been active in professional services. In particular, he was a core member in establishing the New England Statistical Society (NESS) in 2017 and served as its secretary until 2021. Currently, he serves as the Program Chair for the ASA Section on Statistical Computing and Vice-President for the ASA Connecticut Chapter.

Dr. Chen received his B.Econ. in Finance and Dual B.S. in Computer Science & Technology from the University of Science & Technology of China in 2003, M.S. in Statistics from the University of Alaska Fairbanks in 2007, and Ph.D. in Statistics from the University of Iowa in 2011. Before joining UConn, he was on the faculty of Kansas State University from 2011 to 2013.

Dr. Jingjing Ye, BeiGene

Dr. Jingjing Ye Dr. Jingjing Ye is an executive director and currently leads a global team, Data Science and Digital Innovations (DSDI), with Global Statistics and Data Sciences (GSDS) in BeiGene. She has over 17 years’ experience in pharmaceutical industry and US FDA, with focus in cancer drug discovery and development. Her statistical and regulatory experience expands full spectrum on patients’ treatment journey from diagnosis, treatment, to living with the condition. She is very active in statistical communities in US and between US and China DIA communities, including leading several cross-disciplinary and cross-company working groups. She received her PhD degree in statistics from University of California, Davis and BS in applied mathematics from Peking University.

Recent Development for AI/ML for Drug Discovery

Haoda Fu, Eli Lilly and Company

Haoda Fu Dr. Haoda Fu is an Associate Vice President and an Enterprise Lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association), and IMS Fellow (Institute of Mathematical Statistics). He is also an adjunct professor of biostatistics department, Univ. of North Carolina Chapel Hill and Indiana university School of Medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin-Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics and data science methodology research. He has more than 100 publications in the areas, such as Bayesian adaptive de sign, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrika, Bio metrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session. He is a COPSS Snedecor Awards committee member from 2022-2026, and will also serve as an associate editor for JASA theory and method from 2023.

Abstract: In recent years, there has been a growing interest in the application of artificial intelligence (AI) and machine learning (ML) techniques in drug discovery. This is driven by the need for more efficient and effective ways of identifying and developing new drugs, as well as the increasing availability of large datasets and computing power. In this talk, we will review some of the recent developments in AI/ML for drug discovery, including the use of deep learning for drug design, the application of reinforcement learning for optimizing drug combinations, and the use of generative models for chemical synthesis. We will also discuss some of the challenges and limitations of these approaches, and their potential impact on the future of drug discovery. Overall, the use of AI/ML in drug discovery holds great promise for accelerating the development of new treatments and improving patient outcomes.

Panel Discussion

Jared Christensen (Moderator), Pfizer

Jared Christensen Jared Christensen is a Vice President and Head of Internal Medicine and Infectious Disease Statistics within Global Biometrics and Data Management, Pfizer Research & Development. While experienced in all phases of development, he has spent most of his career focused on early clinical trials. Jared has led early teams to embed innovative, decision making tools in early development plans including digital health technologies, AI/ML methodologies, bring your own device and decentralized trials. Jared began his career at Wyeth before moving to Pfizer. He has had roles of increasing responsibility for early clinical and non-clinical work across multiple research units and has worked with statistical leaders to embed machine learning and AI in drug development from screening through phase 2. Jared’s research interests include dose-response modeling, estimands, small sample trial design, missing data and data sharing in the pharmaceutical industry. Jared received his PhD in Biostatistics from Harvard University.

Venkat Sethuraman, BMS

Venkat Sethuraman Venkat Sethuraman, PhD, MBA serves as the senior vice president of Global Biometrics and Data Sciences at Bristol Myers Squibb, where he is accountable for the biostatistics/quantitative support to Global Development and Medical organization. In addition, Venkat leads the Innovation Pillar for the Global Drug Development, developing the data and digital strategy by utilizing data science, advanced clinical trial solutions and robust digital tools to accelerate the pipeline. Venkat and his team are working at the forefront of how Bristol Myers Squibb is utilizing digital innovation to revolutionize drug development by harnessing the power of big data, artificial intelligence (AI) and machine learning approaches to power prediction from the earliest inception of our programs. Prior to ZS, Venkat held various leadership roles at BMS, Novartis Oncology and GSK. Venkat has diverse research interests and consulting experience in industry that includes clinical trials design, innovative trial models, data science, and most recently, digital efforts in clinical research. Venkat received a PhD in statistics from Temple University and an MBA from the Wharton School of the University of Pennsylvania. He currently serves on the Board of Association for Women in Science (AWIS) and has served on the board of the Biopharmaceutical Section of the American Statistical Association.

Hao Zhu, FDA

Hao Zhu Dr. Hao Zhu is the director of the Division of Pharmacometrics, Office of Clinical Pharmacology, Office of Translational Science, Center of Drug Evaluation and Research, U.S. Food and Drug Administration. Dr. Zhu received his Ph.D. in pharmaceutical sciences and Master in statistics from the University of Florida. He started his career in modeling and simulation teams in Johnson & Johnson and Bristol-Myers-Squibb. He joined FDA as a pharmacometrics reviewer more than 16 years ago. Dr. Zhu has been a clinical pharmacology team leader for more than 6 years and a QT-IRT scientific lead for 2 years. Then he became the deputy director at the Division of Pharmacometrics. His division reviews the pharmacometrics related submissions and supports pharmacometrics-related policy development.

Ming-Hui Chen, University of Connecticut

Ming-Hui Chen Dr. Ming-Hui Chen is a Board of Trustees Distinguished Professor and Head of Department of Statistics at University of Connecticut (UConn). He obtained his PhD in Statistics from Purdue University in 1993. He was elected to Fellow of International Society for Bayesian Analysis in 2016, Fellow of Institute of Mathematical Statistics in 2007, and Fellow of American Statistical Association in 2005. He received the University of Connecticut AAUP Research Excellence Award in 2013, the UConn College of Liberal Arts and Sciences (CLAS) Excellence in Research Award in the Physical Sciences Division in 2013, the University of Connecticut Alumni Association's University Award for Faculty Excellence in Research and Creativity (Sciences) in 2014, the ICSA Distinguished Achievement Award in 2020, and the Distinguished Science Alumni Award from Purdue University in 2023. He has published 460+ peer-reviewed journal articles and five books including two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He has supervised 42 PhD students. He served as President of ICSA (2013), Chair of the Eastern Asia Chapter of International Society for Bayesian Analysis (2018), President of New England Statistical Society (2018-2020), and the 2022 JSM Program Chair. Currently, he is Co Editor-in-Chief of Statistics and Its Interface, inaugurated Co Editor-in-Chief of New England Journal of Statistics in Data Science, and an Associate Editor for several other statistical journals.

What Happens After the Model?

Max Kuhn, Posit PBC

Max Kuhn Max Kuhn is a software engineer at Posit PBC (nee RStudio). He is working on improving R’s modeling capabilities and maintaining about 30 packages, including caret. He was a Senior Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He has applied models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics. He and Kjell Johnson wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, recognizing the best book reviewed in Technometrics in 2015. Their second book, Feature Engineering and Selection, was published in 2019, and his book Tidy Models with R, was published in 2022.

Abstract: Machine learning models are everywhere now. We must spend more time on what happens before and after the model fit to build higher-quality algorithms. This talk will describe a set of post-model activities that can improve the fit and also ensure that, when deployed, it is used effectively.