Yu Cheng

Yu Cheng is an Assistant Professor in the Department of Computer Science at Brown University. He holds a Ph.D. from the University of Southern California, advised by Shang-Hua Teng. Prior to his current role, he was a postdoc at Duke University, a visiting member at the Institute for Advanced Study, and an Assistant Professor at the University of Illinois at Chicago. His research focuses on machine learning, optimization, and game theory, with recent emphasis on robust algorithms for machine learning, including high-dimensional robust statistics and non-convex optimization.

He teaches advanced courses such as CSCI2952Q: Robust Algorithms for Machine Learning and CSCI1520: Algorithmic Aspects of Machine Learning. His work has been recognized with the Best Paper Award at WINE 2018. Yu advises Ph.D. students like Binhao Chen and Xing Gao (co-advised with Lev Reyzin), and has mentored undergraduate researchers including Tianle Jiang and Haichen Dong.

His algorithmic contributions span spectral graph theory, mechanism design, and strategic classification. Recent publications address challenges in robust matrix sensing, outlier-robust estimation, and efficient algorithms for participation-constrained planning.

• Affiliations: Brown University, University of Illinois at Chicago (former), Duke University (former postdoc)
• Research Areas: Robust machine learning, non-convex optimization, algorithmic game theory
• Key Projects: Robust algorithms for high-dimensional data, strategic classification systems, spectral graph sparsification