معرفی
Jiawei Zhang is the Michael Armellino Professor in Business and Professor of Information, Operations and Management Sciences at the Leonard N. Stern School of Business, New York University. He chairs the Department of Technology, Operations, and Statistics and serves as Academic Director of the Master of Science in Data Analytics & Business Computing program. He joined NYU Stern in September 2004 and held a joint position at NYU Shanghai from 2014 to 2017.
- PhD in Management Science and Engineering, Stanford University, 2004
- MS in Operations Research, Tsinghua University, 1999
- BS in Applied Mathematics, Tsinghua University, 1996
Professor Zhang's research centers on business analytics, optimization, and operations management. His work integrates machine learning, robust optimization, and stochastic modeling to solve complex problems in supply chain management, healthcare operations, pricing, and revenue management. He investigates decision-making under uncertainty, online learning, and algorithmic approaches to resource allocation.
His recent publications reveal a strong focus on theoretical and applied aspects of stochastic optimization, prophet inequalities, assortment optimization, and process flexibility. These works appear in top journals such as Operations Research, Management Science, and Mathematics of Operations Research, reflecting deep contributions to both methodological innovation and practical applications in operations.
His editorial service highlights scholarly leadership:
- Associate Editor, Management Science (2014–present)
- Associate Editor, Manufacturing & Service Operations Management (2021–present)
- Associate Editor, Mathematics of Operations Research (2009–present)
- Associate Editor, Operations Research (2006–present)
Professor Zhang teaches a range of courses from undergraduate to PhD levels, including Decision Models and Analytics, Convex Optimization, and Dynamic Programming. He advises PhD students and contributes to executive education programs, including in Risk and Decision Analytics and FinTech. His research is supported by theoretical rigor and real-world applicability, with implications for technology, finance, and healthcare operations.
