Brian Athey is the Michael Savageau Professor and founding Chair of the Department of Computational Medicine and Bioinformatics in the U-M Medical School and co-director of the Michigan Institute for Data Science (MIDAS). He also has appointments as a Professor of Psychiatry and of Internal Medicine. Brian has served as Director of Academic Informatics and as Associated Director of the Michigan Institute for Clinical and Health Research.

Brian received his Ph.D. in Cell and Molecular Biology (Biophysics Concentration) at the University of Michigan in 1990, and was trained as a macromolecular structural biologist, where he made seminal contributions to our understanding of chromatin structure. Brian has led many well-known data intensive projects including the National Library of Medicine (NLM) Visible Human Project, the DARPA Virtual Soldier Project, and the NIH National Center for Integrative Biomedical Informatics (NCIBI). He was also the PI of the Michigan Center for Biological Information (MCBI), which helped to establish the current Michigan University Research Corridor ( Brian is currently serving as a co-founder and Chief Science Officer (CSO) of the tranSMART Foundation. tranSMART is the standard open science translational bioinformatics analysis platform used by the pharmaceutical industry. Brian is the Principal Investigator of one of the most well established NIH Training Programs in Bioinformatics in the US. He is an elected fellow of the American College of Medical Informatics (FACMI). He has consulted extensively for the Defense Advanced Research Projects Agency and at the NIH Office of the Directors. He has also been a Peace Fellow of the Federation of American Scientists (

Dr. Athey’s recent research interests are in the creation and use of bioinformatics pipelines and machine learning methods to radically improve the efficacy of psychiatric pharmacogenomics—allowing patients to take the most effective drug for their illness and suffer the fewest side effects. He is also developing new high-throughput methods to analyze images of genes in the context of the cellular nucleus to better understand the machinery of bioinformatics in context.