Research Interests


Data-driven and Robust Optimization 

Dealing with uncertain data. Optimization and decision making under uncertainty. 

Large-Scale Optimization and Decomposition Algorithms

Developing problem-specific decomposition-based algorithms to solve computationally-intensive mathematical programming models. Using heuristics and metaheuristics for solving extremely large-scale applications.

Inverse Optimization

 Finding the optimization parameters for a given optimal solution. Inverse optimization with uncertain observations.



Radiation Therapy Treatment Planning

Radiation therapy is one of the main treatment methods for cancer. The goal is to irradiate cancerous cells with high-energy radiation beams while minimizing the radiation to the healthy tissue. We use robust optimization to mitigate uncertainties in the patient's anatomical features throughout the treatment and find high-quality treatment plans that meet clinically-prescribed criteria while minimizing possible side effects. Radiation therapy optimization problems are extremely large-scale and solving them is computationally intensive. We develop specialized solution algorithms that can be employed to solve these problems efficiently. 


Patient Scheduling

Patients often have to wait for a long time before they receive medical services of different types. This long waiting time may be of critical importance to some patients, depending on the urgency of care they need. The scheduling of patient appointments with different priority levels is therefore challenging, especially since the future arrival rates of patients and their priority levels are uncertain. We employ robust optimization to provide efficient multi-period multi-priority patient scheduling policies that aim to meet certain waiting time thresholds for patients in each priority level and minimize their excess waiting times.