I am a PhD candidate in the Laboratory for Information and Decision Systems (LIDS) at Massachusetts Institute of Technology. I am advised by Professor Asuman Ozdaglar and Devavrat Shah in the Department of Electrical Engineering and Computer Science.
Before coming to MIT, I received my B.S. in Computer Science from California Institute of Technology in 2011. I earned a M.S. in Electrical Engineering and Computer Science from MIT in May 2013. Here is a current copy of my CV.
The goal of my research is to design scalable statistical algorithms for processing social data based on principle from statiscal inference. In our modern world, much of the data is generated by humans and drives decisions made in a variety of settings. Due to the complexities of human behavior, the precise data model is often not obvious, and naive assumptions could drastically effect the outcome of the analysis. This warrants the need for developing a fundamental theory for how to integrate flexible human behavioral models together with statistics and machine learning methods. Recently, I have worked on increasing the flexibility of models and algorithms through an approach of nonparametric regression for latent variable models (aka blind regression). These approaches are related to matrix completion and community detection, and have implications towards applications such as recommendation systems, denoising crowdsourced solutions, and social network analysis. Previously I have also worked on sparse matrix methods, specifically how to exploit sparsity or graph properties to approximate a single component of the solution to a linear system, or the largest eigenvector.
Christian Borgs, Jennifer Chayes, Christina E. Lee and Devavrat Shah. “Sparse graphon Estimation via Neighbor Method.” Paper in submission, 2017.
Christina E. Lee and Devavrat Shah. “Unifying Framework for Crowd-sourcing via Graphon Estimation.” Paper in submission, 2017.
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. Journal version in preparation. Short Video.
Christina E. Lee and Glen Weyl. “Computerized Raffle for Optimal Assignment of Goods.” Patent under submission through Microsoft, Jan 2016.
Christina E. Lee, Asuman Ozdaglar, Devavrat Shah. “Asynchronous Approximation of a Single Component of the Solution to a Linear System.” Paper in submission, 2014.
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.
“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.