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

Static and dynamic infrastructure system monitoring under uncertain time-dependent failures

Multiple power-buses with different states of potential failure must be inspected with UAVs by means of selecting the routes that yield the highest reward.

Risk-averse stochastic optimization for logistic network design and reconfiguration under uncertain lead-time and demand disruptions

A logistic network must be designed completely or partially, such that it is robust against disruptions. Additionally, reconfiguration strategies must exist to recover from extreme disruptions.