معرفی
Yifan Hu is an Assistant Professor in the Department of Statistics at Rutgers University (New Brunswick, NJ). His research focuses on optimal decision-making under uncertainty, with expertise spanning optimization, statistics, and operations research. He works on problems arising from reinforcement learning, large language models, causal inference, and operations management.
Dr. Hu received his PhD in Operations Research from the University of Illinois at Urbana-Champaign in 2022, advised by Prof. Xin Chen and Prof. Niao He. Prior to his PhD, he earned a Bachelor of Mathematics from Nankai University. He also completed postdoctoral work with Prof. Daniel Kuhn from EPFL and Prof. Andreas Krause from ETH Zurich.
Dr. Hu's research centers on developing optimization methods to solve complex problems with provable guarantees. His work addresses challenges such as nonconvexity, noisy observations due to distribution shifts, interactions between multiple decision makers, and limited data samples. He has developed approaches for structured nonconvex optimization problems that can be solved efficiently to global optimality, despite their apparent computational intractability. His research spans several key areas including global optimality in structured nonconvex optimization, large-scale causal inference, contextual bilevel optimization, and robust reinforcement learning with large language models.
Dr. Hu has published numerous papers in top-tier venues including NeurIPS, ICML, ICLR, AISTATS, and journals like SIAM Journal on Optimization and Operations Research. His recent work shows a strong trend toward integrating optimization theory with practical applications in machine learning, causal inference, and decision-making systems. He has made significant contributions to understanding the landscape of nonconvex optimization problems, developing efficient algorithms for causal discovery, and creating robust frameworks for reinforcement learning and language model alignment.
Dr. Hu serves as an area chair for ICLR and as a reviewer for numerous statistics, optimization, and operations research journals (e.g., JASA, JMLR, OR, MS, MSOM, MP, SIAM OPT, Math OR) as well as machine learning conference proceedings (e.g., NeurIPS, ICML, ICLR, AISTATS, COLT). He is actively seeking motivated PhD students and research interns with backgrounds in mathematics, statistics, operations research, or computer science.
Outside of academia, Dr. Hu enjoys skiing, swimming, hiking, and playing tennis.
