Research
My primary research interest lies in data-driven optimization and decision making under uncertainty.
Theories
- Stochastic Optimization
- Distributionally Robust Optimization
- Reinforcement Learning
Applications
- Human-centric Disaster Relief
- Public Health and Medical Decision Making
The following are some of the projects I have been working on
Ensemble Method for Multi-source Stochastic Optimization
A decision-level ensemble framework built on empirical-residuals-based sample average approximation (ER-SAA) for multi-source contextual stochastic optimization
Related outputs (4)
Publications
Presentations
Dynamic Data-driven Preventive Care Allocation Under Multi-source Predictions of Intervention Effectiveness
A dynamic preventive-care allocation framework that combines multiple predictive sources, corrects their biases, updates their reliability over time, and makes robust intervention decisions under uncertainty
Related outputs (1)
Publications
Learning-enhanced Route Recommendation under Unknown and Uncertain Travelers’ Trust
A learning-enhanced optimization framework for route recommendation which explicitly incorporates uncertain travelers’ compliance
Related outputs (1)
Publications