Department of Industrial and Operations Engineering; Department of Urology
Ph.D., Management Science, McMaster University, Canada, 2001
M.Sc., Physics, York University, Canada, 1996
B.Sc., Chemistry and Physics, McMaster University, Canada, 1994
Brian Denton’s research interests are in data-driven sequential decision making and optimization under uncertainty with applications to medicine. He has a cross-appointment in the School of Medicine and he is a member of the Cancer Center and the Institute for Healthcare Policy and Innovation (IHPI) at University of Michigan. He is also a fellow of the Cecil G. Sheps Health Services Research Center at University of North Carolina at Chapel Hill. Previously he was an Assistant and Associate Professor at North Carolina State University (2007-2012), a Senior Associate Consultant at Mayo Clinic (2005-2007), and a Senior Engineer at IBM (2001-2005). In 2009 he received the National Science Foundation Career Award. He has also won the INFORMS Service Section Prize (2010), the INFORMS Daniel H. Wagner Prize (2005), the Institute of Industrial Engineers Outstanding Publication Award (2005), and the Canadian Operations Research Society Best Paper Award (2000). He has served on the Editorial Boards of Health Systems, IIE Transactions, Interfaces, Manufacturing & Service Operations Management, Medical Decision Making, Operations Research, Optimization in Engineering, and Production and Operations Management. He has served as the Medical Decision Making Department Editor for IIE Transactions on Healthcare Systems Engineering from 2008-2015. He has co-authored more than seventy journal articles, conference proceedings, and book chapters, and he has twenty five patents granted by the U.S. Patent and Trademark Office. He is past Chair of the INFORMS Health Applications Section and he served as Secretary of INFORMS (2012-2015). He is currently President of INFORMS.
For additional information about his awards & honors, research, and professional activities, click here for CV.
Areas of Interest
Computational optimization under uncertainty including stochastic programming, simulation-optimization, and Markov decision processes. Data analytic approaches to medical decision making related to the detection, treatment, and prevention of chronic diseases including cancer, cardiovascular disease and diabetes.