Keynote and Plenary Speakers

Whitney Robinson, Duke University School of Medicine

Whitney Robinson is an epidemiologist who uses big data to understand racial/ethnic and gender inequities in health and health care. She is an associate professor in the obstetrics and gynecology department at Duke University School of Medicine. Her ongoing research links health care system and administrative to figure out why Black women in the South are treated with hysterectomy at high rates at relatively young ages. In addition, Robinson often develops novel study design approaches to research population-level inequalities using simple regression-based analytic techniques. Unifying themes of Robinson’s research are intersectionality, the public health critical race praxis, and life course theory. In particular, Robinson thinks Black people are an important sentinel population for health and health care research in the US: The kind of changes that would improve the health of Black Americans would have positive impacts on diverse populations throughout our country.

Sherri Rose, Stanford University

Sherri Rose is a professor of health policy and co-director of the Stanford University Health Policy Data Science Lab. Her research centers on developing and integrating innovative statistical machine learning approaches to improve human health and health equity. Within health policy, Rose works on ethical algorithms in health care, risk adjustment, chronic kidney disease, and health program evaluation. Her honors include the National Institutes of Health Director’s Pioneer Award, National Institutes of Health Director’s New Innovator Award, and Mortimer Spiegelman Award, which recognizes a statistician under age 40 who has made the most significant contributions to public health statistics. In 2024, she was recognized with both the ASHEcon Willard G. Manning Memorial Award for Best Research in Health Econometrics and the American Statistical Association Outstanding Statistical Application Award.

Haoda Fu, Amgen

Haoda Fu—a fellow of the American Statistical Association and Institute of Mathematical Statistics—is head of exploratory biostatistics at Amgen. He is also an adjunct professor in the biostatistics department at The University of North Carolina at Chapel Hill and Indiana University School of Medicine. He has more than 100 publications in areas such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision-making, and rare events analysis and his research has been published in journals such as JASA, JRSS-B, Biometrika, Biometrics, ACM, IEEE, JAMA, and Annals of Internal Medicine. In recent years, his research has focused on machine learning and artificial intelligence.

Susan Paddock, NORC at the University of Chicago

Susan Paddock is chief scientist and executive vice president at NORC at the University of Chicago, where she leads the research science division, advancing NORC’s methods of design and analysis through cross-functional collaboration, efficient statistical operations, and research and development. Her substantive expertise includes health services research—particularly health care quality and performance measurement—and her methodological expertise includes Bayesian methods, causal inference, data integration, hierarchical modeling, and survey methods. Paddock—an American Statistical Association Fellow and 2024–2026 vice president—was honored with the 2013 Mid-Career Award of the ASA Health Policy Statistics Section.