Daniel Jiang

Research Scientist - Core Data Science

I am a research scientist on the Facebook Core Data Science team, where I work on a variety of problems related to sequential decision making under uncertainty. My research interests are in the areas of reinforcement learning, approximate dynamic programming, and Bayesian optimization. I received my PhD in Operations Research & Financial Engineering from Princeton University in 2016 and am also affiliated with the University of Pittsburgh as an assistant professor (currently on leave) in Industrial Engineering.


Reinforcement learning, approximate dynamic programming, Bayesian optimization, operations research

Latest Publications

NeurIPS - December 7, 2020

BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization

Max Balandat, Brian Karrer, Daniel Jiang, Sam Daulton, Ben Letham, Andrew Gordon Wilson, Eytan Bakshy

NeurIPS - December 7, 2020

Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees

Shali Jiang, Daniel Jiang, Max Balandat, Brian Karrer, Jacob R. Gardner, Roman Garnett

ICML - July 12, 2020

Lookahead-Bounded Q-Learning

Ibrahim El Shar, Daniel Jiang