Constance and Martin Silver Endowed Prof in Data Science
New York University
Constance and Martin Silver Endowed Professor in Data Science and Prevention; Director, Constance and Martin Silver Center on Data Science and Social Equity
Dr. Ruopeng An is a leading expert in obesity epidemiology and policy evaluation, a noted interdisciplinary data scientist, and an internationally recognized scholar in applying artificial intelligence to address public health disparities and social inequities.
He currently holds the Constance and Martin Silver Endowed Professorship in Data Science and Prevention and serves as the Director of the Constance and Martin Silver Center on Data Science and Social Equity. Dr. An is also an elected Fellow of the American Academy of Health Behavior and the American College of Epidemiology.
His research has been funded by various federal agencies and public/private organizations, including OpenAI, Abbott, and Amgen. Recognized as one of Elsevier’s top 2% most cited scientists, his work has been featured by major media outlets such as TIME, The New York Times, The Los Angeles Times, The Washington Post, Reuters, USA Today, Bloomberg, Forbes, The Atlantic, The Guardian, FOX, NPR, and CNN. He also serves on research grants and expert panels for the NIH, CDC, NSF, HHS, USDA, and the French National Research Agency.
Before joining NYU, Dr. An was the Faculty Lead in Public Health Sciences and Faculty Fellow for AI Innovations in Education at Washington University in St. Louis, where he also founded two certificate programs focused on artificial intelligence and data science.
Dr. An holds a PhD in Policy Analysis from the Pardee RAND Graduate School, a Master of Public Policy from the National Graduate Institute for Policy Studies, and a BA in Political Science and Public Administration from Peking University.
Areas of Expertise:
Environmental influences and population-level interventions on obesity, weight-related behaviors and outcomes across the life course; Social and economic determinants and policies affecting physical, mental, and cognitive health in children, adults of all ages, and individuals with disabilities; Applications of artificial intelligence and data analytics for public health and social equity; Using data and statistical research methods to evaluate the effects of policies; Systematic review and meta-analysis to identify and appraise existing research.