Research Expertise and Interest

optimization, machine learning, data-driven decision-making

Research Description

Paul Grigas's research interests are broadly in optimization, machine learning, and data-driven decision making, with particular emphasis on contextual stochastic optimization and algorithms at the interface of machine learning and continuous optimization. Paul’s research is funded by the National Science Foundation, including an NSF CRII Award. Paul was awarded 1st place in the 2020 INFORMS Junior Faculty Interest Group (JFIG) Paper Competition and the 2015 INFORMS Optimization Society Student Paper Prize. He received his B.S. in Operations Research and Information Engineering (ORIE) from Cornell University in 2011, and his Ph.D. in Operations Research from MIT in 2016.