Shipra Agrawal is an Associate Professor of the Department of Industrial Engineering and Operations Research at Columbia University. She is also affiliated with the Department of Computer Science and the Data Science Institute. She received her PhD in Computer Science from Stanford University in June 2011 under the guidance of Prof. Yinyu Ye,
and was a researcher at Microsoft Research India from July 2011 to August 2015. Her research spans several areas of optimization and machine learning,
including online optimization under uncertainty, multi-armed bandits, online learning, and reinforcement learning. She is also interested in
prediction markets and game theory.
Shipra serves as an associate editor for Management Science
and Mathematics of Operations Research, INFORMS Journal on Optimization and Journal of Machine Learning Research (JMLR).
Her research is supported by a
Google Faculty research award (2017), Amazon research award (2017),
and an NSF CAREER Award.
For further information, please see CV .
Yunhao Tang (Graduated 2021, research scientist at Google Deepmind London)
Xingyu Zhang (Graduated 2021, research scientist at Amazon)
Randy Jia (Graduated 2020, research scientist at Amazon)
Mohammad Shadravan (Graduated 2020, postdoctoral associate at Yale)
Vashist Avadhanula (Graduated 2018, research scientist at Facebook)
(A full list of journal and conference papers is here )
Dynamic pricing and learning under Bass model, S. Agrawal, S. Yin, A. Zeevi, EC 2021. [arXiv]
Dynamic first price Auctions robust to heterogeneous buyers, S. Agrawal, E. Balkanski, V. Mirrokni, B. Sivan,
EC 2021. [arXiv]
On optimal ordering in the optimal stopping problem, S. Agrawal, J. Sethuraman, X. Zhang. EC 2020. [arXiv]
Reinforcement Learning for Integer Programming: Learning to Cut, Y. Tang, S. Agrawal, Y. Faenza, ICML 2020. [arXiv]
Learning in structured MDPs with convex cost functions: Improved regret bounds for inventory management. S. Agrawal and R. Jia, EC 2019. [arXiv]
Robust Repeated Auctions under Heterogeneous Buyer Behavior. S. Agrawal, C. Daskalakis, V. Mirrokni, B. Sivan, EC 2018. [ arXiv ]
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds. S. Agrawal and R. Jia, NIPS 2017 (spotlight) [ arXiv ]
Thompson Sampling for MNL-bandit. S. Agrawal, V. Avandhanula, V. Goyal, A. Zeevi. COLT 2017. [ arXiv ]
Fast algorithms for online stochastic convex programming. S. Agrawal, N. R. Devanur. SODA 2015. [ EE ] [ arXiv ]
Bandits with concave rewards and convex knapsacks. S. Agrawal, N. R. Devanur. EC 2014. [ EE ] [ arXiv ]
Near-Optimal Regret Bounds for Thompson Sampling JACM 64 Issue 5, October 2017 [ EE ][ pdf ].
(The pdf version here includes a correction made to the published version: in Lemma 2.13, page 15:16. Modified text is in blue)
Thompson Sampling for contextual bandits with linear payoffs. S. Agrawal, N. Goyal. ICML 2013. [ EE ]
[Newer version with improved results is here: arXiv ]
Analysis of Thompson Sampling for the multi-armed bandit problem. S. Agrawal, N. Goyal. COLT 2012. [ pdf ] [ arXiv ]