Committee List

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. Kun 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 a Board Member of the International Chinese Statistical Association.

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. Ming-Hui Chen is Board of Trustees Distinguished Professor and Head of the Department of Statistics at the University of Connecticut (UConn). He was elected to Fellow of International Society for Bayesian Analysis in 2016, Fellow of the Institute of Mathematical Statistics in 2007, Fellow of American Statistical Association in 2005. He also 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, and ICSA Distinguished Achievement Award in 2020. He has published over 428 statistics and biostatistics methodological and medical research papers in mainstream statistics, biostatistics, and medical journals. He has also published five books including two advanced graduate-level books on Bayesian survival analysis and Monte Carlo methods in Bayesian computation. He has supervised or been supervising 37 PhD students. He served as President of the International Chinese Statistical Association (ICSA) in 2013, Program Chair and Publication Officer of SBSS of the American Statistical Association (ASA) and the ASA Committee on Nomination for 2016-2017 to nominate candidates for ASA President/Vice President. Currently, he serves as the 2022 JSM Program Chair, Past President of the New England Statistical Society, 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 of JASA, JCGS, and LIDA.

Dr. Rui (Sammi) Tang is a seasoned drug developer and innovative pharmaceutical leader who has contributed to the successful development and approval of numerous therapies—bringing medicines from research to market that now reach millions of patients every day. With a proven track record of building high-performing teams and driving scientific and operational innovation, she delivers data-driven solutions that accelerate drug development and improve global health outcomes.

As Senior Vice President and Global Head of Quantitative Sciences and Evidence Generation (QSEG) at Astellas Pharmaceuticals, Dr. Tang leads the company’s global data and evidence strategy across quantitative analytics, epidemiology, real-world evidence (RWE), biostatistics, programming, medical writing, scientific communication, data systems & enablement, and data management. She is at the forefront of applying Generative AI in regulatory and clinical documentation, AI/ML-powered analytics, and external data to optimize study design and development efficiency.

She also serves as Site Head of the Astellas Life Sciences Center (ALSC) in Cambridge, where she oversees full site operations and strategic direction across integrated teams including Research, Medical & Development, Business Development, and IT. Under her leadership, the ALSC drives innovation through internal collaboration and external partnerships with incubator labs, biotech start-ups, and academic institutions.

A dedicated scientific leader, Dr. Tang serves on the Executive Committee for Data Science & AI at the American Statistical Association (ASA) and is co-founder of DahShu, a global nonprofit advancing data science research and education with over 5,000 members.

Previously, Dr. Tang was Vice President and Global Head of Biometrics at Servier Pharmaceuticals and Therapeutic Area Head of Biostatistics at Shire. Earlier in her career, she contributed to drug development and statistical innovation at Vertex, Amgen, Mayo Clinic, and Merck—experiences that shaped her cross-functional leadership approach.

Dr. Tang holds a PhD in Statistical Genetics from Michigan Technological University and an Executive MBA from MIT Sloan. She is also an Adjunct Professor at Yale University School of Public Health. With over 50 peer-reviewed publications and multiple patents, she is widely recognized for combining scientific depth with strategic leadership to deliver transformative therapies that improve lives worldwide.

Yong Chen is a Professor of Biostatistics and Founding Director of the Center for Health AI and Synthesis of Evidence (CHASE) at the University of Pennsylvania, where he leads research in clinical evidence generation and synthesis using real-world data. He also directs the Penn Computing, Inference, and Learning (PennCIL) lab, focusing on developing methods for integrating clinical data. Dr. Chen is serving as a Statistical Editor for the Annals of Internal Medicine, a Statistical Consultant for New England Journal of Medicine-AI, and an Associate Editor for both the Journal of the American Statistical Association – Applications and Case Studies (JASA-ACS) and The Annals of Applied Statistics (AoAS). Dr. Chen has authored over 200 peer-reviewed papers in statistics and medical informatics. His work centers at evidence synthesis, machine learning/AI, and clinical evidence generation. He is an elected Fellow of the American Statistical Association and the American College of Medical Informatics, with joint appointments in Applied Mathematics and at the Penn Institute for Biomedical Informatics.

Dr. Roee Gutman is a Professor in the Department of Biostatistics at Brown University. His areas of expertise are record linkage, causal inference, missing data, Bayesian data analysis and their application to big data sources. Dr. Gutman has authored multiple papers in which he developed novel methods to analyze linked data sources, and for estimating causal effects from observational studies. These methods were applied to address clinical, epidemiological, and health services and policy questions, especially among the elderly.

Dr. Gutman has co-authored over 100 publications, including papers in leading statistical and subject-matter journals. His contributions have been recognized with the ISPOR Health Economics and Outcomes Research Methodology Award for his work on estimating the causal effects of Meals on Wheels programs on healthcare utilization using linked datasets. He served as an ASA/NSF/BLS Senior Research Fellow, collaborating with researchers at the Bureau of Labor Statistics to develop novel record linkage techniques. He is a member of CNSTAT at the National Academies of Sciences, Engineering, and Medicine.

Chengxing (Cindy) Lu, Ph.D. is a senior director of Oncology Biometrics in AstraZeneca. She focused her over 15 years of career in drug discovery and development, led statistical aspects for multiple compounds across various disease areas, from early to late phases of clinical development to post-marking activities, resulting in several successful regulatory approvals. In her current role in AstraZeneca, she is supervising a group of statisticians overseeing a broad variety of early phase oncology assets. Dr. Lu is currently leading multiple cross-industry initiatives sponsored by DahShu and ASA. She is the co-founder and co-chair of ASA-DahShu IDSWG Joint Master Protocol Multidisciplinary Scientific Working Group (MPWG), the co-lead of a sub-team in ASA Biopharmaceutical (BIOP) Section Oncology SWG. Dr. Lu was the co-chair for International Society of Biopharmaceutical Statistics (ISBS) symposium in 2024 and will be the co-chair for ASA BIOP Regulatory-industry Statistical Workshop in 2026.

Zhaohua Lu is an Associate Director in Biostatistics at Daiichi Sankyo and a Ph.D.-trained statistician with over ten years of experience in statistical modeling, data science, and clinical trial design and implementation. Prior to this role, he served as a Faculty Member in the Biostatistics Department at St. Jude Children’s Research Hospital for six years.

His expertise spans the analysis of large neuroimaging, genomic, and natural language datasets, with a strong focus on predictive modeling, machine learning, Bayesian methods, and statistical computations and simulations. He has authored approximately 40 peer-reviewed clinical papers and 30 methodological publications, contributing significantly to both applied and methodological advancements in biostatistics.

Dr. Nalini Ravishanker is Professor in the Department of Statistics at the University of Connecticut (UConn), Storrs, https://statistics.uconn.edu/. She has been a faculty member at UConn since 1989. She has a PhD in Statistics and Operations Research from the Stern School of Business, New York University, and a B.Sc. in Statistics from Presidency College, Madras. Nalini has over three decades of academic experience and is passionate about research, teaching and mentoring students in statistics and data science. Her primary area of research is time series analysis. Her research productivity includes over 140 publications, two books, and one edited volume - on various topics in methodological and applied statistics and interdisciplinary domains. She has taught statistical theory, methods, computing, and applications at various levels. She enjoys mentoring students and junior faculty, and promoting the usefulness of data science to industry professionals. She has supervised 15 PhD advises and 15 Undergraduate researchers. She has consulted on statistical topics for different industries, both in the US and India. She is a Fellow of ASA, AAAS, CASE, and elected member of ISI. She will serve as President-elect of ISI from 2025-2027. She is currently EIC of the journal Applied Stochastic Models in Business and Industry. She is committed to enhancing the role of statistics and data science in scientific investigations in different domains such as civil and transportation engineering, climate, ecology, environment, finance, insurance, marine science, marketing, etc.