Shiqian Ma is a professor in the Department of Applied Mathematics and Statistics and a member of the Data Science and AI Institute (DSAI) at Johns Hopkins University. He is also affiliated with the Department of Computer Science and the Department of Electrical and Computer Engineering.
His research focuses on the mathematical foundations of modern AI, with particular emphasis on optimization, geometry, and machine learning. He develops scalable optimization algorithms and mathematical frameworks for efficient and reliable training of foundation models and large-scale machine learning systems.
Ma currently serves as an action editor for the Journal of Machine Learning Research (JMLR) and Transactions on Machine Learning Research (TMLR), and an associate editor for SIAM Journal on Optimization (SIOPT) and Journal of Optimization Theory and Applications (JOTA). He has served as a senior area chair for NeurIPS, ICML, and AISTATS, among other leading machine learning conferences.
His research is supported by the National Science Foundation and the Office of Naval Research. His work on Riemannian optimization has been recognized by the 2024 INFORMS Computing Society Prize and the 2024 SIAM Review SIGEST Award.
Ma received his BS in mathematics from Peking University, MS in computational mathematics from Chinese Academy of Sciences, and PhD in operations research from Columbia University.