I am the CEO and Director of OptiMax AI, where we are building a data-driven AI supply chain management platform. I also serve as an Honorary Research Assistant Professor at the Department of Data and Systems Engineering, The University of Hong Kong, and remain actively engaged in academic research, collaborating with Prof. Max Zuo-Jun Shen and other researchers. Previously, I was a Postdoctoral Fellow at HKU (2023-2026) and an Instructor at Rutgers University (2019-2022).
I got my Ph.D. degree, with the concentration on Operations Research, from the Department of Management Science and Information Systems at Rutgers University, under the supervision of Prof. Jian Yang. Before that, I received my M.Sc. in Information Technology and Analytics from Rutgers University, under the supervision of Prof. Hui Xiong, and my B.Sc. in Electrical Engineering and B.A. in Business Administration from University of Electronics Science and Technology of China (UESTC).
Research Interests: Pricing and Revenue Management, Inventory Control, Game-theoretic Applications, Data-driven Modeling, Transportation, AI in Operations Management
Committee: Andrzej Ruszczynski, Endre Boros, Thomas Lidbetter, and Ming Hu
Adviser: Hui Xiong
An Adaptive Model-based Deep Reinforcement Learning Approach for Matching-while-learning Problem in Shared Manufacturing Platforms Liu Yang, Yuan Qu, Pengyu Yan, Chengbin Chu International Journal of Production Research, 64(9):3495-3517, 2026.
Everyone Contributes! Incentivizing Strategic Cooperation in Multi-LLM Systems via Sequential Public Goods Games Yunhao Liang, Yuan Qu, Jingyuan Yang, Shaochong Lin, Max Z.J. Shen Proceedings of AAMAS, 2026. Available on arXiv
How Many Are Too Many? Analyzing Dockless Bikesharing Systems with a Parsimonious Model Hongyu Zheng, Kenan Zhang, Macro Nie*, Pengyu Yan*, Yuan Qu Transportation Science, 58(1):152-175, 2023.
Enactive Artificial Intelligence: A Decision-Centric Architecture for Complex Systems Max Z.J. Shen, Yuan Qu, Pujun Zhang, Anbang Liu, Yunhao Liang, Feier Yan Under Review, Available on arXiv.
Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm Yunhao Liang, Pujun Zhang, Yuan Qu, Jingyuan Yang, Shaochong Lin, Max Z.J. Shen Under Review, Available on arXiv.
SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination Yunhao Liang, Xianqi Cao, Pujun Zhang, Yuan Qu, Yongzhi Qi, Ningxuan Kang, Max Z.J. Shen Under Review, Available on arXiv.
Who Should Pay for the Sharing? Mechanism Design of Resource Allocation in Headquarters-Subsidiary Networks Shan Li, Yuan Qu, Shaochong Lin, Ming Dong, Ran Liu, and Max Z.J. Shen Under Review.
Opaque Selling to Strategic Buyers Yuan Qu*, Jian Yang Under Review. Available on SSRN
Reinforcement Learning for Dynamic Matching on Shared Manufacturing Platforms under Non-stationary Arrivals Liu Yang, Pengyu Yan, Dong Li, Yuan Qu Under Review.
Dynamic Inventory-price Control during Opaque Selling of Horizontally-differentiated Products Yuan Qu*, Jian Yang Under Review. Available on SSRN
Diminishing Social Capital Returns from Employee Ambassadorship: Evidence from WeChat Chaitanya Kaligotla, Sarah Wittman, Jingyuan Yang, Yuan Qu, Wei Zeng Under Review, Available on SSRN
Research on Cooperation Mechanism behind AI Harness with Xinxue (Shawn) Qu, Jingyuan Yang, Pujun Zhang, Yunhao Liang, Feier Yan, and Max Z.J. Shen
Operation Strategy for Styling Box Platform with Max Z.J. Shen
Personalized Online Learning Framework for Styling Box Recommendation with Pujun Zhang and Max Z.J. Shen
Charge Smarter, Not Harder: The Delicate Balance of Shared E-Bike Operations with Lingyun Zhang, Shaochong Lin, and Max Z.J. Shen
Dynamic Operation Management Considering Customer Engagement for Styling Box Platform with Pujun Zhang and Max Z.J. Shen
How Large Are Too Large? Deployment Region Analysis of Dockless Electronic Bike-Sharing Systems with Pengyu Yan and Max Z.J. Shen
One Size Does Not Fit All: Personal Match and Marketing Message Effectiveness on Social Networks with Jingyuan Yang and Wei Zeng
Collaborated in research principally with Professor Max Z.J. Shen of the Department of Data and Systems Engineering, or other researchers as appropriate. Advanced Industrial Engineering Technology Seminar 2024-25, IMSE7002, Spring 2025
Product and Operations Management, Undergraduate, 29:623:311, Fall 2022 Management Information System, Undergraduate, 33:136:370, Spring 2020 - Spring 2022 Statistical Methods for Business, Undergraduate, 33:136:385, Fall 2019 Business Data Management, Graduate, 26:198:603 & 26:544:603, Fall 2019
Built an offline evaluation pipeline for online recommendation algorithms.
Business Data Management, Graduate, 544:603, Spring 2018 Data Mining, Graduate, 198:650, Spring 2018
Develop web system and stock evaluation system. Collect all financial report data from SZSE & SSE. Estimate stock price by using absolute & relative value assessment model. Develop stock investment strategy system. Build auto-investment system based on specific strategy.
Operation Strategy for Styling Box Platform The Seventeenth International Conference of CSAMSE, Chengdu, China, 2025 INFORMS Annual Meeting, Seattle, WA, 2024 Invited Research Seminar, UESTC, Chengdu, China, 2024
Inventory Control involving Opaque Selling 14th POMS-HK International Conference, Hong Kong, 2024 INFORMS Annual Meeting, Phenix, AZ, 2023
Understanding Opaque Selling from an Inventory-control Perspective Invited Research Seminar, UESTC, Chengdu, China, 2023 Northeast Decision Sciences Institute Annual Conference, Washington, DC, 2023 INFORMS Annual Meeting, Indianapolis, IN, 2022 Invited Research Seminar, Stony Brook University, Virtual, 2020
Drivers For Personal Social Marketing Effectiveness: A Joint View Of Content And Diffusion INFORMS Annual Meeting, Virtual, 2020 Invited Research Seminar, UESTC, Chengdu, China, 2020 Rutgers-NJIT Business School Joint Ph.D. Seminar, Newark, NJ, 2019 INFORMS Annual Meeting, Seattle, WA, 2019
Organizing Committee & Session Chair, The Seventeenth International Conference of CSAMSE, Chengdu, China, 2025
Session Chair, INFORMS Annual Meeting, Seattle, WA, 2024