Keynote Speakers

Keynote presentations will offer perspectives on the opportunities and challenges at the intersection of statistics, AI, and health.

Keaven M. Anderson, PhD

Keaven AndersonMerck & Co., Inc.

Dr. Keaven M. Anderson develops statistical methods and open-source software that help clinical researchers turn accumulating trial data into reliable evidence about treatments. As Scientific Associate Vice President of Methodology Research in Biostatistics at Merck, his work spans survival analysis, multiple testing, and group sequential designs, which enable planned interim evaluations while accounting for statistical uncertainty. He is the creator and principal developer of the R package gsDesign and its web interface, connecting methodological advances with practical tools for trial design and analysis. His current interests include supporting open-source R software for regulatory use. A Fellow of the American Statistical Association, he brings experience across pharmaceutical research and the Framingham Heart Study, with doctoral training at Stanford University.

Pandu Kulkarni, PhD

Pandu KulkarniEli Lilly and Company

Dr. Pandu Kulkarni is Chief Executive Officer of Aparito, a wholly owned subsidiary of Eli Lilly and Company, and serves as Lilly’s Chief Analytics Officer for Research and Development and Senior Vice President of Statistics, Data, and Analytics. His work connects statistical methods, digital health technologies, and advanced analytics to improve clinical research and advance the development of new medicines. His work has included advancing applications of artificial intelligence and machine learning in drug development, alongside the organizational and data capabilities needed to put these approaches into practice. Since joining Lilly in 2000, he has held numerous technical and management roles. Previously a tenured professor at the University of South Alabama, he brings experience across academic and industry settings. He is a Fellow of the American Statistical Association and earned his doctorate in statistics at La Trobe University.

Susan A. Murphy, PhD

Susan MurphyHarvard University

Dr. Susan A. Murphy develops statistical methods and learning algorithms that help translate health data into personalized treatment decisions. As the Mallinckrodt Professor of Statistics and of Computer Science at Harvard University, she leads the Statistical Reinforcement Learning Lab, where her team studies how interventions can adapt to an individual’s changing needs. Her work connects reinforcement learning, experimental design, and clinical research to develop and evaluate digital interventions delivered through mobile devices. These collaborations address a central challenge in AI-enabled health care: learning when and how to intervene using evidence gathered over time. A former University of Michigan faculty member, she is a MacArthur Fellow and a member of the National Academy of Medicine and the National Academy of Sciences.