Christina Lee Yu

Assistant Professor
Operations Research and Information Engineering


Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering. Prior to Cornell, she was a postdoc at Microsoft Research New England. She received her PhD in 2017 and MS in 2013 in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in the Laboratory for Information and Decision Systems. She received her BS in Computer Science from California Institute of Technology in 2011. She received honorable mention for the 2018 INFORMS Dantzig Dissertation Award. She is a recipient of the 2021 Intel Rising Stars Award and a JPMorgan Faculty Research Award

Research Interests

Her research interests include algorithm design and analysis, high dimensional statistics, inference over networks, sequential decision making under uncertainty, online learning, and network causal inference.

Selected Publications

  • Sean R. Sinclair, Siddhartha Banerjee, and Christina Lee Yu. “Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve.” ACM SIGMETRICS, 2022.
  • Sean R. Sinclair, Tianyu Wang, Gauri Jain, Siddhartha Banerjee, and Christina Lee Yu. “Adaptive Discretization for Model-Based Reinforcement Learning.” Advances in Neural Information Processing Systems, 2020.
  • Sean Sinclair, Siddhartha Banerjee, and Christina Lee Yu. “Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces.” Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2019.
  • Christian Borgs, Jennifer Chayes, Devavrat Shah, and Christina Lee Yu. “Iterative Collaborative Filtering for Sparse Matrix Estimation.” Operations Research, 2021.
  • Yihua Li, Devavrat Shah, Dogyoon Song, Christina Lee Yu. “Nearest Neighbors for Matrix Estimation Interpreted as Blind Regression for Latent Variable Model.” IEEE Transactions on Information Theory, 2019.
  • Devavrat Shah and Christina Lee Yu. “Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation.” Proceedings of Allerton Conference on Communication, Control, and Computing, 2019. Also presented at International Symposium on Information Theory, 2019.

Selected Awards and Honors

  • 2021 Intel® Rising Stars Award
  • 2021 JPMorgan Faculty Research Award
  • 2020 Simons Institute Research Fellow for Theory of Reinforcement Learning Program
  • 2018 INFORMS Dantzig Dissertation Award Honorable Mention


  • Ph.D. (Electrical Engineering & Computer Science), Massachusetts Institute of Technology, 2017