Brian Denton is a Professor in the Department of Industrial & Operations Engineering, and a member of the Institute for Healthcare Policy and Innovation. His primary research interests are in optimization under uncertainty with applications to medical decision-making. He uses stochastic programming, simulation-optimization and Markov decision processes to optimize decisions regarding detection, treatment, and prevention of chronic diseases, including cancer, diabetes and heart disease.
Rafael Meza is an Assistant Professor in the Department of Epidemiology, School of Public Health, and an Honorary Professor at the Mexico National Institute of Public Health (INSP). Dr. Meza’s is interested in cancer risk assessment and the analysis of cancer epidemiology data using mechanistic models of carcinogenesis. He is also interested in the mathematical modeling of chronic and infectious disease dynamics and its applications in public health policy design.
Dr. Meza is Coordinating Principal Investigator of the Cancer Intervention and Surveillance Modeling Network (CISNET) lung group, Co-Leader of the Cancer Epidemiology and Prevention Program of the Cancer Prevention and Control Program at the University of Michigan Comprehensive Cancer Center (UMCCC), and member of the UM Tobacco Research Network.
Currently, Dr. Meza is developing models to evaluate the impact of screening and smoking cessation on lung cancer risk. Additional projects include the development of methodologies to investigate the effects of infectious disease dynamics on the risk of cancers with infectious disease etiology, modeling the impact of policies on cigarette and smokeless tobacco use, and modeling the impact of diabetes prevention strategies in Mexico.
Amy Cohn is an Arthur F. Thurnau Professor, Professor of Industrial and Operations Engineering, and of Health Management and Policy, and Associate Director for the Center for Healthcare Engineering and Patient Safety. Her research focus is on developing models and algorithms for solving large-scale applied problems in healthcare, aviation, and other industries. She focuses predominantly on scheduling and resource allocation problems, with an emphasis on solutions that are not only provably optimal (or near-optimal) in theory, but that are implementable and sustainable in practice. This research is highly data-driven and based on deep multi-disciplinary collaborations.