معرفی
Cong Ma is an Assistant Professor in the Department of Statistics at the University of Chicago. He holds a PhD from Princeton University (2020), advised by Yuxin Chen and Jianqing Fan, and a BEng from Tsinghua University (2015). Previously, he was a postdoctoral researcher at UC Berkeley under Martin Wainwright.
His research focuses on the mathematics of data science, emphasizing reinforcement learning, transfer learning, multi-modal learning, high-dimensional statistics, and nonconvex optimization. Key areas include developing computationally and statistically efficient methods for large-scale data problems, with recent work on contrastive learning, contextual bandits, and robust matrix completion.
- Education:
- PhD in Operations Research & Financial Engineering, Princeton University (2020)
- BEng in Electrical Engineering, Tsinghua University (2015)
- Awards:
- SIAM Activity Group on Imaging Science Best Paper Prize (2024)
- Teaching:
- STAT 253/317: Introduction to Probability Models (Winter 2025)
- STAT 28000: Optimization (Winter 2025)
- Machine Learning (Autumn 2022)
His work bridges theoretical insights with practical applications, often addressing challenges in statistical guarantees for optimization algorithms and distributionally robust learning. Recent highlights include contributions to multi-modal contrastive learning and batched contextual bandits.
