October 14, 2022
Room 1163 Mechanical Engineering
Markov decision process (MDP) models have been used to obtain and evaluate the performance of policies in various domains, such as treatment planning in medical decision making. However, in practice, decision makers may prefer other alternatives that are not statistically different from the actions in their initial policy of interest. To allow for decision makers’ expertise and provide flexibility in implementing policies, this talk introduces a framework for identifying sets of similar performing actions in finite MDP models.
Professor Marrero’s research interest lies at the intersection of operations research and statistics, with an emphasis on stochastic simulation and optimization to support decision making in practice. His current work addresses various application areas, including substance use disorder, cardiovascular disease, and organ transplantation. Through this research, Marrero has ongoing collaborations with the Massachusetts General Hospital, the University of Michigan Medical School, the University of Michigan School of Public Health, and the U.S. Department of Veterans Affairs. Before joining Dartmouth, he was a postdoctoral research fellow at the MGH Institute for Technology Assessment and Harvard Medical School.