Data and uncertainty
Learn from demand, customer behavior, and evolving information to support reliable decisions.
Operations · Algorithms · AI
Tongwen Wu
I am a postdoctoral fellow at the Rotman School of Management, University of Toronto, mainly working with Professor Ming Hu and Professor Shreyas Sekar. I received my PhD in Decision Analytics and Operations from City University of Hong Kong under the supervision of Professor Yanzhi Li.
Algorithms and AI for data-driven business decisions
I develop optimization and algorithmic methods for revenue management and online platforms. My research connects computer science and artificial intelligence with operational decision-making, using approximation and online algorithms to turn complex, uncertain problems into tractable decisions with rigorous performance guarantees.
- Data-driven decision-making
- Revenue management
- Online algorithms
- Platform design
Academic path
Experience and education
- Present
- Postdoctoral Fellow Rotman School of Management
- 2023
- PhD, Decision Analytics and Operations City University of Hong Kong
- 2019
- BSc, Computer Science and Technology Sun Yat-sen University
- 2016–17
- ICPC & CCPC gold medals Qualified for the ICPC World Finals
Research directions
From data and uncertainty to better decisions
Provable algorithms
Design scalable approximation and online algorithms with meaningful performance guarantees.
Business applications
Improve assortment, inventory, fulfillment, revenue, and operations on digital platforms.
Selected research
Recent work
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EC ’26
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Minor revision · Operations Research
Joint Inventory and Assortment Optimization under General Choice Models
With Qiaochu Fu, Woonghee Tim Huh, and Yanzhi Li.
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Major revision · M&SOM
A Sequential Multi-Purchase Markov Chain Choice Model: Identification, Estimation and Assortment Optimization
With Woonghee Tim Huh, Menglong Li, and Yanzhi Li.
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Operations Research
Constant-Factor Algorithms for Revenue Management with Consecutive Stays
With Ming Hu. Accepted.
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Published online · M&SOM
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Reject & resubmit · Operations Research
Background
Algorithms as a foundation
My training in algorithms began as an undergraduate in Computer Science and Technology at Sun Yat-sen University. As a competitive programmer, I earned gold medals in ICPC and CCPC regional contests in 2016 and 2017 and qualified for the ICPC World Finals. Solving difficult problems under strict time and memory limits taught me to identify useful structure, reason carefully under constraints, and turn mathematical ideas into efficient implementations.
I now bring that foundation to operations and revenue management. My work develops approximation and online algorithms for settings where demand and information arrive over time. Recent results establish constant-factor guarantees where earlier bounds were nonconstant and deliver state-of-the-art performance guarantees for online assortment decisions.
I am also interested in how data and AI can inform decisions without giving up rigor. Across platform design, assortment and inventory planning, fairness, fulfillment, and nonprofit operations, I study data-driven models and algorithms with predictions that combine empirical information with transparent performance guarantees.
Contact
Contact
Rotman School of Management
University of Toronto