معرفی
Junyu Cao is an Assistant Professor in the Department of Information, Risk, and Operations Management at McCombs School of Business, The University of Texas at Austin. He earned his Ph.D. in Industrial Engineering and Operations Research from UC Berkeley in 2020. His research focuses on data-driven decision making, online learning, stochastic modeling, and their applications to revenue management, recommender systems, smart-city analytics, and operations.
- Ph.D.: Department of Industrial Engineering and Operations Research, UC Berkeley
- Education: B.S. in Mathematics, Zhejiang University; B.S. in Industrial Engineering, Zhejiang University; Exchange Program, UCLA
His work spans interdisciplinary domains including sequential decision-making in dynamic environments, bandit problems, and network modeling. He has developed algorithms for adaptive data acquisition, fatigue-aware recommendation systems, and urban mobility optimization.
Recent publications in Management Science, Operations Research, and NeurIPS highlight his contributions to pricing optimization, reinforcement learning, and transportation systems. His research has been recognized with multiple awards including the IBM PhD Fellowship and Katta Murty Best Paper Prize.
- Finalist in MSOM Service Management SIG Best Paper Award, 2024
- Second Place, POMS CHOM Best Paper Competition, 2024
- Winner, INFORMS ISS Cluster Best Paper Award, 2022
Junyu has advised two Ph.D. students at UT Austin and served as Graduate Student Instructor at UC Berkeley. He has received editorial and judging roles in prestigious academic competitions.
