Mohsin Shah is the Associate Director of Global Integrated Evidence - Epidemiology & RWE Oncology at Boehringer Ingelheim, where he collaborates cross-functionally to assist in the development of strategies and real-world studies to enhance clinical development, add to regulatory submissions, inform payer interactions, and respond to changes in the marketplace, and enhance internal decision making. Dr. Shah is a clinical pharmacoepidemiologist by training with over eight years of experience conceptualizing, designing, and leading real-world studies. He has published over 26 peer-reviewed articles and presented over 20 conference presentations. Dr. Shah completed a T-32-funded postdoctoral fellowship in applied pharmacoepidemiology and graduated with a Master of Science in Clinical Epidemiology (MSCE) degree from the Perelman School of Medicine, University of Pennsylvania. Before this training, he completed a postdoctoral fellowship in internal medicine and oncologic emergency medicine at the UT MD Anderson Cancer Center. He received his medical doctorate from the University of Health Sciences. Dr. Shah’s expertise lies in (a) evaluating novel interventions using real-world data and state-of-the-art biostatistical and missing data methods (b) generating real-world evidence from such novel interventions, and (c) optimizing pharmacoepidemiology research methods to adapt to patients with cancer.
Dr. Watson is an infectious diseases physician-scientist whose clinical HIV research program focuses on accelerating equitable HIV pre-exposure prophylaxis (PrEP) service delivery and eliminating stigma and discrimination in sexual health, with particular attention to Black LGBTQ+ adults in the United States. She has particular expertise in clinical ID epidemiology, health equity, HIV prevention research, LGBTQ+ Health, and community-engaged research. Dr. Watson’s work has led to several publications delineating provider-level, structural, and systemic barriers and facilitators to equitable PrEP equity for patients with multiple marginalized identities. She is the recipient of institutional and federal grants from the National Institutes of Health, including a Career Development Award (K23) from the National Institute of Mental Health, which an equity-focused implementation science grant to design and pilot a novel multifaceted implementation strategy to address provider behaviors and organization-level practices that affect PrEP equity for Black patients in Philadelphia. Dr. Watson’s long-term goal is to eliminate behavioral and structural barriers to PrEP implementation within clinical and community settings in order to achieve PrEP equity among Black and LGBTQ+ populations.
Dr. Watson received her B.A. degree from Cornell University and her medical degree from the Northwestern University. She completed her internship and residency at the University of Chicago, during which time she also completed a Medical Ethics fellowship at the University of Chicago MacLean Center for Clinical Medical Ethics. She subsequently completed her Infectious Diseases (ID) fellowship and obtained her Master of Science in Clinical Epidemiology (MSCE) degree from the University of Pennsylvania.
Sean Hennessy, PharmD, PhD, to be awarded the 2024 Kenneth Rothman Career Accomplishment Award at the 2024 SER Conference (June 18-21) in Austin, TX. Read more.
Andrea Schneider, MD, PhD, Assistant Professor of Neurology & Epidemiology, has been selected by the NIH Office of Disease Prevention as a 2024 Early Stage Investigator Lecture awardee. Read more.
Congratulations to John H. Holmes, PhD, FACE, FACMI, Professor of Medical Informatics in Epidemiology, whom was selected to receive a 2024 Christian R. and Mary F. Lindback Award for Distinguished Teaching. Read more.
Dr. Jin’s research focuses on developing statistical methods and computational tools to address cutting-edge problems in public health and medicine by integrating large-scale, multi-modal data. Her research areas include health equity issues in disease risk prediction, statistical genetics, and Bayesian hierarchical models for high-dimensional, complex-structured data.
