Rate My Professor Tengyu Ma

Tengyu Ma is an Assistant Professor of Computer Science at Stanford University, holding a joint appointment in the Department of Statistics since 2018. He received his Ph.D. in Computer Science from Princeton University, advised by Sanjeev Arora, and completed his undergraduate studies at Tsinghua University. During his graduate studies, Ma was awarded the Siebel Scholarship and Princeton Honorific Fellowship, and he achieved 8th place in the 2010 William Lowell Putnam Mathematical Competition as well as a Silver Medal in the 2007 International Mathematical Olympiad. His research spans machine learning, algorithms and their theory, non-convex optimization, deep learning and its theory, reinforcement learning, pre-training and foundation models, robustness, distributed optimization, and high-dimensional statistics. Ma employs techniques from theoretical computer science, applied mathematics, statistics, probability, and information theory to design nonlinear models and analyze nonconvex optimization landscapes in areas such as dictionary learning, matrix completion, tensor decomposition, and neural networks.

Ma's influential publications include "On the opportunities and risks of foundation models" (2021), "Learning imbalanced datasets with label-distribution-aware margin loss" (NeurIPS 2019), "A simple but tough-to-beat baseline for sentence embeddings" (ICLR 2017), "Generalization and Equilibrium in Generative Adversarial Nets (GANs)" (ICML 2017), "What learning algorithm is in-context learning? Investigations with linear models" (2022), and "Matrix Completion has No Spurious Local Minimum" (NeurIPS 2016), the latter earning the NeurIPS Best Student Paper Award. He has received the Sloan Research Fellowship (2021), Samsung AI Researcher of the Year (2022), NSF CAREER Award, ACM Doctoral Dissertation Award Honorable Mention (2018), and COLT Best Paper Award (2018). Ma teaches CS 229/STATS 229: Machine Learning and supervises advanced research projects. He serves as Area Chair for conferences such as NeurIPS (2019-2021), ICLR (2019-2021), and COLT (2020-2021), and is a member of Bio-X and Faculty Affiliate of the Institute for Human-Centered Artificial Intelligence (HAI).