Ph.D. Operations Research & Financial Engineering, Princeton University, 2016
M.A. Operations Research & Financial Engineering, Princeton University, 2013
B.S. Computer Engineering, With Highest Distinction, Purdue University, 2011
B.S. Mathematics, With Highest Distinction, Purdue University, 2011
Zhan, W., Fujimoto, S., Zhu, Z., Lee, J.D., Jiang, D.R., & Efroni, Y. (2024). Exploiting Structure in Offline Multi-Agent RL: The Benefits of Low Interaction Rank.
Wang, Y., & Jiang, D.R. (2023). Faster Approximate Dynamic Programming by Freezing Slow States.
Benjaafar, S., Jiang, D., Li, X., Li, X. (2022). Dynamic Inventory Repositioning in On-Demand Rental Networks. MANAGEMENT SCIENCE, 68(11), 7861-7878.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/mnsc.2021.4286.
Astudillo, R., Jiang, D.R., Balandat, M., Bakshy, E., & Frazier, P.I. (2021). Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs.
Benjafaar, S., Jiang, D., Li, X., Li, X. (2021). Dynamic Inventory Repositioning in On-Demand Rental Networks. Management Science.
Balandat, M., Karrer, B., Jiang, D., Daulton, S., Letham, B., Wilson, A., & Bakshy, E. (2020). BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. Advances in Neural Information Processing Systems (NeurIPS 2020).
Jiang, D.R., Al-Kanj, L., & Powell, W.B. (2020). Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds. Operations Research, 68(6), 1678-1697.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/opre.2019.1939.
Jiang, S., Jiang, D., Balandat, M., Karrer, B., Gardner, J., & Garnett, R. (2020). Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees. Advances in Neural Information Processing Systems (NeurIPS 2020).
Shar, I.E., & Jiang, D.R. (2020). Lookahead-Bounded Q-Learning. International Conference on Machine Learning (ICML 2020), PartF168147-12, 8624-8634.
Jiang, D.R., & Powell, W.B. (2018). Risk-averse approximate dynamic programming with quantile-based risk measures. Mathematics of Operations Research, 43(2), 554-579.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/moor.2017.0872.
Jiang, D.R., Ekwedike, E., & Liu, H. (2018). Feedback-Based Tree Search for Reinforcement Learning. International Conference on Machine Learning (ICML 2018).
Johnson, A.L., & Jiang, D.R. (2018). Shape Constraints in Economics and Operations Research. STATISTICAL SCIENCE, 33(4), 527-546.Institute of Mathematical Statistics. doi: 10.1214/18-STS672.
Jiang, D.R., Al-Kanj, L., & Powell, W.B. (2017). Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds.
Jiang, D.R., & Powell, W.B. (2016). Practicality of Nested Risk Measures for Dynamic Electric Vehicle Charging.
Jiang, D.R., & Powell, W.B. (2015). Optimal hour-ahead bidding in the real-time electricity market with battery storage using approximate dynamic programming. INFORMS Journal on Computing, 27(3), 525-543. doi: 10.1287/ijoc.2015.0640.
Jiang, D.R., & Powell, W.B. (2015). An Approximate Dynamic Programming Algorithm for Monotone Value Functions. Operations Research, 63(6), 1489-1511.Institute for Operations Research and the Management Sciences (INFORMS). doi: 10.1287/opre.2015.1425.