Christina Lee Yu

Christina Lee Yu 

Assistant Professor
Operations Research and Information Engineering (ORIE)
Graduate field member in ORIE, Statistics, CAM, and CS
Cornell University

Office: 226 Rhodes Hall
Email: cleeyu (at) cornell (dot) edu
Google Scholar
CV

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

News

  • Jan 2023. Our paper Robust Max Entrywise Error Bounds for Sparse Tensor Estimation via Similarity Based Collaborative Filtering has been accepted to IEEE Transactions on Information Theory.

  • Oct 2022. Our paper Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure has been published in the Proceedings of the National Academy of Sciences.

  • Sept 2022. Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve is a finalist for 2022 INFORMS Diversity, Equity, and Inclusion Student Paper Competition.

  • Sept 2022. Our paper Adaptive Discretization for Online Reinforcement Learning has been accepted to Operations Research.

  • Sept 2022. Our paper Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve has been accepted to Operations Research.

  • Sept 2022. I received the Ralph S. Watts ‘72 Excellence in Teaching Award.

  • Sept 2022. Our paper Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge has been accepted to NEURIPS 2022.

  • June 2022. Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure received a Best Poster Award from ACM SIGMETRICS 2022.

  • June 2022. I gave a tutorial on “Causal Inference in the Presence of Network Interference” at CORS/INFORMS International Conference 2022.

  • Apr 2022. Our paper Nonparametric Matric Estimation with One-Sided Covariates has been accepted to ISIT 2022.

  • Mar 2022. Our paper Tensor Estimation with Nearly Linear Samples Given Weak Side Information has been accepted to ACM SIGMETRICS 2022.

  • Oct 2021. Our paper Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve has been accepted to ACM SIGMETRICS 2022.

  • July 2021. My proposal Exploiting Low Rank Structure for Provably Efficient Reinforcement Learning was selected for the JPMorgan Faculty Research Award.