Jian Wu 吴鉴

Department of Decision Analytics and Operations, College of Business, City University of Hong Kong 83 Tat Chee Ave, Kowloon Tong, Hong Kong Email: jian.wu [@] cityu.edu.hk

Bio

I am an Assistant Professor of Management Sciences in the College of Business, City University of Hong Kong. Before joining CityU, I obtained my Ph.D. from Mitch Daniels School of Business at Purdue University, under the supervision of Prof. Qi Annabelle Feng, Prof. J. George Shanthikumar, and Prof. William B. Haskell. My research focuses on decision-making under uncertainty in supply chain management.

Perspective Students

I am currently seeking highly motivated Ph.D. students who possess a solid mathematical foundation and are passionate about data analytics and supply chain management. If you hold a bachelor's or master's degree in mathematics, probability, statistics, computer science, or industrial engineering, please reach out by emailing me your CV and academic transcripts.

Education

Purdue University, 2019 - 2025
Ph.D. in Operations Management

The University of Hong Kong, 2015 - 2019
B.Eng in Industrial Engineering and Technology Management (First Class Honor)

The University of Wisconsin-Madison, January - May 2018
Exchange Student, International Academic Programs (IAP)

Journal Publications

1. Solving the Price-Setting Newsvendor Problem with Parametric Operational Data Analytics (ODA). Management Science (with L. Y. Chu, Q. Feng, J. G. Shanthikumar and Z. J. M. Shen) [pdf]

   We study joint pricing and ordering when the demand model's parameters are unknown and data is limited. Our operational data analytics (ODA) approach learns the decision directly from data, yielding more robust profits than conventional estimate-then-optimize methods.

2. Contextual Data-Integrated Newsvendor Solution with Operational Data Analytics (ODA). Management Science (with Q. Feng and J. G. Shanthikumar) [pdf]

   We extend the data-integrated newsvendor framework to settings where demand depends on contextual covariates. Using equivariant decision rules and an adaptive-boosting approach, we obtain inventory policies that remain robust even when the demand distribution is unknown.

3. Robust Data-Driven Quasiconcave Optimization. INFORMS Journal on Computing (with W. B. Haskell, W. Huang and H. Xu) [pdf]

   We maximize an unknown quasiconcave objective using only a finite sample of data points. We solve the resulting robust optimization problem efficiently via binary search over upper level sets, with strong performance on production-efficiency and fair-allocation applications.

Working Papers

1. Efficiently Computing the Quasi-Concave Envelope with Incomplete Information. Under major revision, SIAM Journal on Optimization. (with W. B. Haskell, W. Huang and H. Xu) [pdf]

   We compute the quasiconcave envelope of a function observed only at limited data points, subject to properties such as monotonicity and Lipschitz continuity. Our two-step procedure solves the value and interpolation problems with only a polynomial and a logarithmic number of linear programs, respectively.

2. Operational Data Analytics (ODA) Embedded Neural Network Learning for the Newsvendor Problem with General Demand. Work in progress (with Q. Feng and J. G. Shanthikumar)

   We study the contextual newsvendor problem when demand depends nonlinearly on high-dimensional covariates and data is scarce. We embed operational data analytics into neural network learning via data augmentation and learned covariate transformations, outperforming conventional benchmarks in the small-sample regime.

3. Proximal Systems for Inventory Management under Positive Lead Times. Submitted (with Q. Feng, J. G. Shanthikumar and Y. Yang)

   We study lost-sales inventory systems with positive lead times, where order history and stockouts make optimal control difficult. We introduce proximal systems that reinterpret performance accounting to derive simple, asymptotically optimal policies, then interpolate between them with a single tuning parameter.

Teaching

Instructor, MS6721 Supply Chain Management, Spring 2026, City University of Hong Kong

Instructor, MGMT361 Operations Management, Spring 2024, Purdue University

Teaching assistant, MGMT565 Strategic Sourcing and Procurement, Fall 2021, Fall 2022, Purdue University

Teaching assistant, MGMT568 Supply Chain Analytics, Summer 2022, Purdue University

Teaching assistant, MGMT664 Supply Chain Management, Summer 2021, Purdue University

Honors and Awards

• Bilsland Dissertation Fellowship, Purdue University

• DSB Certificate for Outstanding Teaching, Purdue University

• DSB Doctoral Student Research Funds 2022, 2024, Purdue University

• Krannert Award for Doctoral Study in Supply Chain & OM 2019-20, Purdue University

• China Soong Ching Ling Foundation Scholarships 2015-19

Service

Session Chair, INFORMS Annual Conference 2022, 2023, 2024, POMS Annual Conference 2025, 2026

Reviewer for Management Science, Manufacturing & Service Operations Management, Production and Operations Management, INFORMS Journal on Computing, Naval Research Logistics, International Journal of Production Research