Research
My primary research interest lies in applied mathematical optimization and machine learning, including:
- Theories: Stochastic and distributionally robust optimization, reinforcement learning, integer programming
- Applications: logistic network risk management, scheduling, integrated systems inspection and maintenance
- Computational Methods: distributed computation, parallel computation
The following are some of the projects I have been working on
Dynamic investment planning and operation of power systems under wildfire-prone conditions
Multi-stage and dynamic optimization models for grid resilience under wildfire risk.
Static and dynamic infrastructure system monitoring under uncertain time-dependent failures
Inspection routing for vulnerable infrastructure under uncertain potential failure states.
Risk-averse stochastic optimization for logistic network design and reconfiguration
Robust logistics network design and recovery under demand and lead-time disruptions.