Philip Payne, PhD, FACMI, FAMIA
Janet and Bernard Becker Professor, Associate Dean, Office of Health Information and Data Science, Founding Director, Institute for Informatics
Chief Data Scientist, Washington University School of Medicine, St Louis

Dr. Payne is the Janet and Bernard Becker Professor and Founding Director of the Institute for Informatics at Washington University in St. Louis. He is also the Associate Dean for the Office of Health Information and Data Science as well as Chief Data Scientist for Washington University. He holds appointments as a Professor of General Medical Sciences and Computer Science and Engineering in the Schools of Medicine and Engineering and Applied Sciences respectively. In this capacity, he is responsible for the creation and oversight of comprehensive biomedical informatics and data science research, training, and support programs aligned with the health and life science enterprise spanning Washington University, BJC Healthcare, and a variety of regional partners. Further, he serves as the director of the Biomedical Informatics components/programs that exist under the auspices of both the CTSA-funded Institute for Clinical and Translational Science (ICTS) and the NCI-funded Siteman Cancer Center at Washington University. He earned both masters and doctoral degrees in Biomedical Informatics at the Columbia University College of Physicians and Surgeons. He is an elected fellow of the American College of Medical Informatics (ACMI) and the American Medical Informatics Association (AMIA) and also holds leadership appointments on numerous national steering, editorial, and advisory committees, including efforts associated with AMIA, Association for Computing Machinery (ACM), National Cancer Institute (NCI), National Library of Medicine (NLM), and the National Center for Advancing Translational Science (NCATS). His research portfolio broadly focuses upon the areas of translational bioinformatics (TBI) and clinical research informatics (CRI) and includes projects focusing on: 1) knowledge-based approaches to high-throughput hypothesis discovery and data-driven decision making; 2) distributed data management and analysis in support of clinical and translational research; and 3) human-factors and workflow analysis.