Differentially Private Matrix Completion through Low-rank Matrix Factorization
I am currently a Research Assistant Professor at the Toyota Technological Institute at Chicago. I recevied my Ph.D. in Department of Computer Science at the University of California, Los Angeles, where I was advised by Professor Quanquan Gu. Previously I obtained my MS in Statistics at University of Washington.
My research interests are broadly in machine learning including privacy-preserving machine learning, optimization, federated learning, deep learning, low-rank matrix recovery, high-dimensional statistics and data mining.
Boxin Zhao was a PhD student in Econometrics and Statistics at University of Chicago, Booth School of Business. His research interests include probabilistic graphical models, functional data analysis and distributed learning, with a focus on developing novel methodologies with both practical applications and theoretical guarantees.
