Lauren Steimle is a Ph.D. candidate in Industrial and Operations Engineering at the University of Michigan. Her research interests are in operations research and data analytics with a focus on computational optimization and stochastic modeling for solving decision-making problems under uncertainty. Her current research interests are motivated by challenges in decision-making for public health, and her dissertation research has focused on stochastic optimization methods for the design of personalized, data-driven treatment recommendations for cardiovascular disease under data uncertainty and model ambiguity.
Steimle is the recipient of the National Science Foundation Graduate Student Research Fellowship, a member of the third-place team in the New England Journal of Medicine’s SPRINT Data Challenge, and an Honorable Mention for the Ford Foundation Predoctoral Fellowship. She has served as President of the University of Michigan’s INFORMS Student Chapter and as Outreach Officer of the Graduate Society of Women Engineers. Steimle earned her M.S.E. in Industrial and Operations Engineering from the University of Michigan in 2016 and holds a B.S. in Systems Science and Engineering from Washington University in St. Louis.
Academic Advisor: Brian Denton
Position sought: Academic
Expected Graduation: May 2019
Student Lecturer (Fall 2017): IOE 202 – Operations Modeling