Dr. Rui (Sammi) Tang is a is an innovative, results-oriented pharmaceutical leader with a proven track record of building high-performing teams, driving innovation, and delivering cost-effective, data-driven solutions in drug development.
As Senior Vice President and Global Head of Quantitative Sciences and Evidence Generation at Astellas Pharmaceuticals, she shapes the company’s data strategy and leads global teams across quantitative analytics, epidemiology, real-world evidence (RWE), biostatistics, programming, medical writing, scientific communication, data System&enablement, and data management. She is at the forefront of integrating Generative AI for regulatory and clinical trial documentation, AI/ML-driven solutions, and external data utilization to optimize trial design and accelerate drug development.

Previously, Dr. Tang served as Vice President and Global Head of Biometrics at Servier Pharmaceuticals, advancing AI-driven analytics to enhance drug development, and as Therapeutic Area Head of Biostatistics at Shire Pharmaceuticals, overseeing key functions across multiple therapeutic areas. She also held pivotal roles at Vertex, Amgen, Mayo Clinic, and Merck, contributing to statistical advancements that have supported the approval of life-saving therapies impacting millions worldwide.

Dr. Tang holds a PhD in Statistical Genetics from Michigan Technological University and an Executive MBA from MIT Sloan. She is an Adjunct Professor at Yale University School of Public Health and co-founder of DahShu, a non-profit organization advancing data science research and education with over 5,000 members globally. With 50+ peer-reviewed publications and multiple patents  in adaptive trial design and precision medicine, she brings deep expertise in statistical innovation and clinical development.  Her career exemplifies a strategic, data-driven approach to innovation, combining leadership, advanced analytics, and cross-functional collaboration to deliver impactful therapies to patients worldwide.

 Dr. Shuangge (Steven) Ma is a Professor of Biostatistics at Yale School of Public Health. His research interests include genetic epidemiology, EHR data analysis, cancer biostatistics, and deep learning. He obtained his Ph.D. in Statistics from the University of Wisconsin, Madison in 2004. He was a Postdoctoral Fellow at the University of Washington, Seattle between 2004 and 2006 and has been at Yale University afterwards.

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.

Haiyan Huang received her BS in Mathematics at Peking University in 1997, her PhD in Applied Mathematics at the University of Southern California in 2001. She did postdoc at Harvard University from 2001-2003. Currently, she is a Professor and the Chair of the Department of Statistics at UC Berkeley. She was the Director of the Center for Computational Biology at UC Berkeley from 2019 to 2022. 

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.