Christina Lee Yu (formerly Christina E. Lee)

cleeyu (at) cornell (dot) edu
Christina Lee Yu 

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

Office: 226 Rhodes Hall

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. Her recent interests include matrix and tensor estimation, multi-arm bandits, and reinforcement learning.

Link to CV.

Publications

(If prefaced by * then authors are ordered alphabetically)

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. Will be presented at ACM Sigmetrics conference 2020.

*Nirandika Wanigasekara and Christina Lee Yu. “Nonparametric Contextual Bandits in an Unknown Metric Space.” To appear in Advances in Neural Information Processing Systems, 2019.

*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. Journal paper in submission.

*Devavrat Shah and Christina Lee Yu. “Reducing Crowdsourcing to Graphon Estimation, Statistically.” International Conference on Artificial Intelligence and Statistics, 2018.

*Christian Borgs, Jennifer Chayes, Christina E. Lee and Devavrat Shah. “Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation.” Advances in Neural Information Processing Systems, 2017. Journal version in submission. Short Video. Poster.

*Christina E. Lee, Yihua Li, Devavrat Shah, Dogyoon Song. “Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering.” Advances in Neural Information Processing Systems, 2016. Short Video.

*Asuman Ozdaglar, Devavrat Shah, and Christina Lee Yu. “Asynchronous Approximation of a Single Component of the Solution to a Linear System.” IEEE Transactions on Network Science and Engineering, 2019.

*Christina E. Lee, Asuman Ozdaglar, and Devavrat Shah. “Computing the Stationary Distribution Locally.” Advances in Neural Information Processing Systems, 2013. Journal version in submission.

Elizabeth Bodine-Baron, Christina Lee, Anthony Chong, Babak Hassibi and Adam Wierman. “Peer effects and stability in matching markets.” Proceedings of Symposium on Algorithmic Game Theory, 2011.

Working Papers

Christina Lee Yu, Edo Airoldi, Christian Borgs, and Jennifer Chayes. “Measuring Treatment Effects in the Presence of Additive Network Interference Effects.” working paper.

Presentations

‘‘Tensor Estimation with Nearly Linear Samples.’’ Talk at Information Theory and its Applications in San Diego, Feb 2020.

‘‘Nonparametric Contextual Bandits in an Unknown Metric Space.’’

‘‘Adaptive Discretization for Sequential Decision Making in Large Continuous Spaces.’’

‘‘Predictions in Excel through Estimating Missing Values.’’ Invited Workshop at Open Data Science Conference, May 2019.

‘‘Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation.’’

Tutorial on Matrix Estimation at International Symposium on Information Theory, June 2018.

“Iterative Collaborative Filtering for Sparse Matrix Estimation”

“Approximation of a Single Component of the Solution to a Linear System.” Presented at the Workshop on Graphical Models, Statistical Inference, and Algorithms, hosted by UMN Institute for Mathematics and its Applications, May 2015. Video. Slides.

Awards

INFORMS Dantzig Dissertation Award Honorable Mention (2018)

Claude E. Shannon Research Assistantship (2016-2017)

NSF Graduate Research Fellowship (2013-2016)

MIT Irwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowship (2011-2012)

Teaching

Fall 2020 SYSEN 5200 Systems Analysis Behavior and Optimization

Spring 2019 ORIE 3800 Information Systems and Analysis

Fall 2018 ORIE 6700 Statistical Principles