Daniel Jiang

Daniel Jiang is an accomplished academic and researcher in the fields of industrial engineering, operations research, and computer science. With a tenure-track position as an Assistant Professor in the Department of Industrial Engineering at the University of Pittsburgh from January 2019 to September 2021, Daniel also held an adjunct faculty role during that period. Research interests include approximate dynamic programming, reinforcement learning, and Bayesian optimization, with applications extending to renewable energy, public health, and the sharing economy. Prior experience includes an undergraduate research internship at Stanford University focused on algorithm design and a Ph.D. candidacy at Princeton University, where the dissertation centered on dynamic programming methods. Currently serving as a Research Scientist at Meta, Daniel holds both a Ph.D. and a Master of Arts in Operations Research and Financial Engineering from Princeton University, along with dual Bachelor's degrees in Computer Engineering and Mathematics from Purdue University, both achieved with highest distinction.