Thirteen projects across six themes

Precise modeling of patient states in critical care — essential for timely intervention and outcome prediction — remains constrained by two fundamental challenges: (1) existing models struggle to inte

Primary care clinicians spend large amounts of their working hours, both in- and outside of the clinic, updating electronic health record (EHR) documentation for patients.

We study healthcare scheduling across the patient care pathway, from surgery to post-discharge home care.

Most inventory models address demand uncertainty but give limited attention to lead time uncertainty, particularly endogenous lead time uncertainty, and often ignore stockpile policies and large-scale

Healthcare systems face increasing pressure from demand surges caused by pandemics, natural disasters, and seasonal illnesses.

This project utilizes data-driven optimization and machine learning methods to improve the quality of inpatient fall risk assessment and decision-making.

This work focuses on brain stereotactic radiosurgery for patients with multiple metastases to identify a single "isocenter" location, the point the machine rotates around as it delivers radiation from

This project tackles a practical bottleneck in radiation therapy planning: even with modern planning software, clinicians often have to "tune" dose-volume limits for tumors and nearby organs through r

This project develops a preference-aware inverse optimization + clustering framework for precision nutrition, aimed at producing diet recommendations that jointly reflect (i) expert dietary constraint

This work follows a healthcare-systems question across multiple waves of COVID-19 in the United States that "as clinical care, variants, vaccination, and hospital strain changed, did the factors linke