Research Interests

  • Applications of Operations Research and Analytics in Healthcare
  • Mathematical Modeling and Optimization of Cancer Therapy
  • Stochastic Control and Real-time Optimization
  • Large-scale and Nonlinear Optimization
  • Stochastic Modeling and Queueing Theory

Current Research Projects

Radiotherapy Planning for Real-time Organ Motion Management

Radiotherapy is one of the most effective and commonly used modalities for cancer treatment. However, if unaccounted for, internal organ motion during radiation delivery may lead to underdosing of cancer cells or overdosing of normal tissue. This could potentially cause treatment failure or normal-tissue toxicity. Organ motion is of particular concern in the treatment of lung and abdominal cancers, where breathing induces large tumor displacement and organ deformation. A recent technological innovation is a new generation of radiotherapy systems equipped with on-board magnetic resonance imaging (MRI) scanners providing a real-time high-contrast movie of the patient's anatomy during radiation delivery. This offers the opportunity to devise a fundamentally new organ-motion management approach in which the radiotherapy plan actively learns and adapts to anatomical variation in real time. This research will develop the methods to enable use of real-time MRI visualization to control the progress of radiation delivery in order to correct for any dose discrepancy, thus allowing treatment plans to actively adjust to anatomical changes during irradiation. The project is carried out in collaboration with the Department of Radiation Oncology at Massachusetts General Hospital and Washington University School of Medicine in St. Louis.

Spatiotemporal Radiotherapy Plan Optimization

This research aims at quantifying the extent of potential therapeutic gain that can be achieved from altering the radiation dose distribution over treatment sessions in fractionated radiotherapy - a concept known as spatiotemporal planning. The design of optimal spatiotemporal radiotherapy plans gives rise to large-scale non-convex treatment-plan optimization problems. Customized global optimization methods are developed and tested to solve the treatment planning problem with small optimality gaps in a clinically reasonable computational time.

Optimal Nurse Staffing and Skill-Mix Decisions in Inpatient-Care Settings

The objective of this research is to develop model-driven staffing strategies for nursing care delivery in inpatient care settings. Traditionally, nurse-to-patient ratios have been used to staff inpatient care units, which specify the number of patients that can be safely supervised by a nurse. However, patients often require different levels of care based on the severity of their medical conditions. Furthermore, not all care tasks need the support of highly trained registered nurses and thus hospitals often employ nursing staff with different skill levels for cost-saving purposes. The heterogeneity in patient-mix and nursing skill-mix can potentially render ratio-based staffing strategies ineffective. We incorporate this heterogeneity into staffing decisions using Queueing Theory and discrete-event simulation methods to determine optimal nursing skill-mix configurations that minimize staffing costs while ensuring timely delivery of care.

For more information visit Health Systems Engineering (HSE) Laboratory.