01
Optimization
Mathematical models and scalable algorithms for complex, uncertain, and resource-constrained systems.
Combinatorial optimizationSubmodular optimizationRouting and schedulingRobust and stochastic optimization
Research directions
Optimization meets intelligence.
We develop models, algorithms, and learning methods for complex systems where structure, uncertainty, and human goals all matter.
02
Artificial Intelligence
Learning, prediction, and intelligent decision-making methods that work with real-world constraints.
Machine learningReinforcement learningFederated learningLLMs and multi-agent systems
From theory to impact
Our research themes are deliberately connected. Optimization provides structure for intelligent decision-making, while AI helps models learn from data and adapt to changing environments.
- Designing algorithms that scale to large and complex instances.
- Combining learning and optimization for data-driven decisions.
- Building methods that remain robust under uncertainty.
- Translating research outcomes into practical systems and collaborations.