Hanbin Yang
Operations Research | Optimization Under Uncertainty
I am currently a Research Fellow in the Department of Information Systems, Business Statistics and Operations Management (ISOM) at the Hong Kong University of Science and Technology, working with Prof. Guodong Lyu.
I received my Ph.D. in Data Science from The Chinese University of Hong Kong, Shenzhen, and my B.S. in Statistics from Southern University of Science and Technology.
Research Interests
My research lies at the intersection of optimization under uncertainty and data-driven decision making, with a focus on mixed-integer programming, decomposition and cutting-plane methods, and AI-based algorithm design for large-scale optimization.
- Stochastic and distributionally robust optimization
- Mixed-integer programming and decomposition algorithms
- Learning-augmented optimization methods
- Applications in power systems and energy operations
Current Focus
- Globally convergent cutting-plane algorithms for multistage stochastic mixed-integer programs
- Efficient approximation methods for chance-constrained optimization with ambiguity
- AI-assisted algorithm selection and acceleration in branch-and-cut frameworks
Research Style
I enjoy research that combines rigorous theory, scalable algorithm design, and practical impact. I am especially interested in projects where methodological advances can be validated through convincing computational experiments and real-world motivated applications.