Dr. Ming-Hui Chen, University of Connecticut

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 (nestat.org), 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. Kun Chen, University of Connecticut

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. Jeff Palmer, Pfizer

Jeff Palmer has been a statistics group head leading early clinical development in rare diseases at Pfizer for the past 5 years. Prior to Pfizer he had worked for over ten years with various other pharma and consulting companies supporting mainly rare diseases, oncology, and neurology. Jeff received his MS in statistics from the University of Chicago and conducted his doctoral research in statistics at Carnegie Mellon University.

Dr. Yang Song, Vertex

Dr. Yang Song is currently Executive Director, Biostatistics Group Head for Pipeline Development, at Vertex Pharmaceuticals Inc., leading biostatistics teams supporting multiple rare disease pipeline development projects. Prior to joining Vertex, he was with Merck for nearly 10 years, rotated through its PA, Beijing, and NJ global sites, with increasing responsibilities for global drug development across multiple therapeutic areas. He also worked with Johnson & Johnson for oncology drug development early in his career. His research interests include rare disease clinical trial methodology, real world evidence, data integration, optimal clinical development strategy, statistical issues in oncology clinical trials, biomarker endpoints, and subgroup analyses. He is a member of the ASA Biopharmaceutical Scientific Working Group on Real World Evidence. Yang received his Ph.D. in Statistics from the University of Wisconsin - Madison.

Dr. Rui (Sammi) Tang, Astellas

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.

Dr. Richard Zhang, Pfizer

Richard Zhang is the Statistics Group Lead for late phase clinical development in rare diseases at Pfizer. He has been in the pharmaceutical industry for over twenty years with exposure to hundreds of clinical trials spanning multiple therapeutical areas: Neuroscience, Pain, Rheumatology, Endocrine and IEM. He has extensive knowledge and experience in regulatory interactions and submissions. His research interests include innovative trial design, real world evidence, meta-analysis, data mining and modeling. Richard received his PhD in statistics from the University of Kentucky.

Dr. Yingwen Dong, Sanofi

Yingwen Dong is the Global Head of Biostatistics in Rare Diseases and Rare Blood Disorders at Sanofi. Prior to this role, she served as the Deputy Global Head of Oncology Biostatistics in late phase at Sanofi. She has over 16 years of clinical development experience in pharmaceutical industry in multiple therapeutic areas including neurology, oncology, rare diseases and rare blood disorders. Her research interests are in the area of innovative clinical trial design and its application. She currently serves as ICSA representative on 2024 JSM programming committee, and the steering committee member for 2024 ASA biopharmaceutical section regulatory-industry statistical workshop. She received her Ph.D in statistics from University of Minnesota.

Dr. Haiying Wang, University of Connecticut

HaiYing Wang is an Associate Professor in the Department of Statistics at the University of Connecticut. He was an Assistant Professor in statistics at the University of New Hampshire from 2013 to 2017. He obtained his Ph.D. from the Department of Statistics at the University of Missouri in 2013, and his M.S. from the Academy of Mathematics and Systems Science, Chinese Academy of Sciences in 2006. His research interests include informative subdata selection for big data, model selection, model averaging, measurement error models, and semi-parametric regression.

Dr. Andy Chi, Takeda

Andy Chi currently serves as Executive director of Statistics and Quantitative Sciences (SQS), Data Sciences Institute (DSI) at Takeda. He is a member of DSI Leadership team (LT) and SQS management team (MT), supporting development and commercialization of Takeda Oncology Portfolio. He has 18 years of drug development experience, and has been strong advocate for innovative trial design and quantitative decision making using diverse trial types and data sources in clinical trials, from Academia and RWD/RWE. Andy received his MS in biometry from University of Texas, Houston, and PhD in Medical Science from University of South Florida.

Dr. Ran Duan, Vertex​

Ran Duan is currently the Director of biostatistics at Vertex Pharmaceuticals oversee multiple indications. Before join Vertex, Ran worked at Angitia, Alexion, AstraZeneca Rare Disease and Eli Lilly and Company, where she supported the clinical development in multiple therapeutic areas including Bone disease, neurology, ophthalmology, and diabetes programs. She is an active member of the ASA Gene and Cell therapy working group. Her research interests include the innovative trial design for gene and cell therapy, rare disease, RWE generation and digital solutions for health care.

Dr. Roee Gutman, Brown University

Dr. Roee Gutman is a Professor in the Department of Biostatistics at Brown University. His areas of expertise are causal inference, file linkage in the absence of unique identifiers, missing data, Bayesian data analysis and their application to big data sources in health services research. He has vast experience in analysing many types of secondary datasets from various sources (e.g. Medicare claims data, registries, VA health data), as well as data collected through large pragmatic randomized trials. Dr. Gutman has been the principal investigator of multiple NSF, NIH and PCORI grants and is the lead statistician on NIH, PCORI and VA grants. Recently, he received ISPOR Health Economics Outcomes and Research - Methodology Award and he was ASA/NSF/BLS Senior Research Fellow.

Dr. Hao Li, Boehringer Ingelheim

Dr. Li currently holds the position of Principal Clinical Data Scientist at Boehringer Ingelheim. In her current role, Dr. Li leads a Biostatistics and Data Science development team as the Product Owner for clinical portfolios developed for rare disease indications in inflammation. Dr. Li's academic credentials include a Master of Science (M.S.) and a Doctorate (Ph.D.) in Statistics, both earned from the University of Connecticut, Storrs. She achieved these degrees in December 2017 and August 2018, respectively. Beyond her work at Boehringer Ingelheim, Dr. Li actively contributes to the broader scientific community by serving as a reviewer for several academic journals and extending her work to publications.

Dr. Junxiang Luo, Moderna

PhD in Biostatistics, Sr. director of biostatistics, rare disease TA leader in Moderna with enriched 18 years’ experience in biopharmaceutical industry for clinical trial design, study implementation, NDA/BLA submission, and regulatory interaction across multiple TAs.

Dr. Susie Sinks, Biogen

Dr. Susie Sinks is currently a Director in Development Statistics at Biogen, where she has served as program lead in neuromuscular, multiple sclerosis and immunology therapeutic areas. Before joining Biogen in 2019, Susie worked in the FDA over 5 years for the Division of Metabolic and Endocrinology Products (DMEP) with specialty in diabetes and metabolic statistical review after receiving a Ph.D. in Biostatistics from Virginia Commonwealth University. Her research interests include missing data, surrogacy modeling, benefit and risk assessment.

Dr. Lin Wang, Insmed

Dr. Lin Wang currently serves as the Vice President and Head of Biometrics at Insmed. Prior to joining Insmed, She spent more than 17 years at Sanofi where she held positions of increasing responsibility, including as Global Biostatistics Head for Rare Diseases and Rare Blood Disorders. Lin has extensive clinical development experience in all stage of clinical development from pre-IND, global NDA/MAA submissions to approvals across multiple diseases for small, large molecules and gene therapies. Her research interests include rare disease clinical trial design and analysis methodology, count data, adaptive design. Lin earned her Ph.D. in Statistics from the University of Wisconsin-Madison.

Dr. Xin Wang, Bristol Myers Squibb

Dr. Xin Wang is Senior Director in BMS leading Cell Therapy Breyanzi Franchise Biometrics Team. Xin received her Ph.D in Statistics from Northwestern University, since then she has 16+ years of experience in pharmaceutical industry. Prior to joining BMS in 2021, Xin was TA Lead of Rheumatology at AbbVie where she held positions with increasing responsibilities with 5 years as people manager. Xin has led the Rheumatology statistics team with significant contributions to the submission and approval of Rheumatology indications in Rinvoq and Humira. Prior to AbbVie, Xin had worked at Pfizer and Sanofi in Inflammation, CVMED and internal medicine. Her research interest includes multiple comparisons, gatekeeping procedures, dose-finding, missing data imputations, and adaptive designs.