Christina Lee Yu
Biography
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. Her work is supported by grants from the National Science Foundation and the Air Force Office of Scientific Research. 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.
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
- Mayleen Cortez, Matthew Eichhorn, and Christina Lee Yu. “Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Designs.” Journal of Causal Inference, 2023.
- Mayleen Cortez, Matthew Eichhorn, and Christina Lee Yu. “Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge.” Advances in Neural Information Processing Systems, 2022.
- Tyler Sam, Yudong Chen, and Christina Lee Yu. “Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure.” Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2023.
- Christina Lee Yu, Edo Airoldi, Christian Borgs, and Jennifer Chayes. “Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure.” Proceedings of National Academy of Sciences, 2022.
- Sean R. Sinclair, Gauri Jain, Siddhartha Banerjee, and Christina Lee Yu. “Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve.” Operations Research, 2022.
- Sean R. Sinclair, Siddhartha Banerjee, and Christina Lee Yu. “Adaptive Discretization for Online Reinforcement Learning.” Operations Research, 2022.
Selected Awards and Honors
- 2024 NSF CAREER Award
- 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
Education
- Ph.D. (Electrical Engineering & Computer Science), Massachusetts Institute of Technology, 2017