Guokai Li 李国凯 [Email]
I am a Postdoctoral Fellow working with Prof. Stefanus Jasin (UMich), Prof. Murray Lei (Queen's), and Prof. Alys Liang (McGill). I received my Ph.D. degree from The Chinese University of Hong Kong, Shenzhen, where I was very fortunate to be supervised by Prof. Zizhuo Wang and Prof. Pin Gao. Prior to that, I obtained my bachelor's degree (ranked 1st) in Industrial Engineering from Xi'an Jiaotong University.
I am passionate about developing practical and deployable algorithms for real-world decision-making. My research is broadly related to revenue management and online resource allocation, and I am gradually broadening the scope of my research.
Recently, I have been working on topics at the intersection of operations research (OR) and artificial intelligence (AI). In particular, I focus on leveraging AI methodologies to solve complex OR problems (AI4OR) and implementing OR algorithms or insights to improve AI system performance (OR4AI). If you are interested in my research and would like to discuss it further, please feel free to catch me at a conference or drop me an email. 😁
Research Interests
Methodologies: asymptotic analysis, online resource allocation, graph neural network, generative AI.
Applications: revenue management & pricing, AI for OR, OR for AI.
News:
The paper "When to push ads" has been accepted by Manufacturing and Service Operations Management (M&SOM) [UTD24 journal].
The journal version of the "GCN for assortment" paper has been updated. In this paper, we illustrate how to use graph neural networks to learn patterns of optimal assortments from small-scale samples, and then utilize the learned patterns to efficiently solve large-scale constrained assortment problems in seconds, which is referred to as the "from small to large" idea.
