Daniel Jiang

RESEARCH SCIENTIST | NEW YORK CITY, UNITED STATES

Daniel Jiang is a Research Scientist at Meta, where he focuses on reinforcement learning and its applications. More broadly, his research interests are in the area of sequential decision-making and also includes the topics of approximate dynamic programming, Bayesian optimization, and adaptive experimentation. Daniel received his Ph.D. from Princeton University in Operations Research and Financial Engineering.