Title: Optimization Driven Machine Learning for Healthcare
Date: Monday, November 9, 2026
Time: 4 PM (ET)
Location: George Tower 1502

 

Himadri S. Pandey
Machine Learning PhD Candidate
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Committee

1. Dr. Gian-Gabriel P. Garcia (Advisor), Industrial and Systems Engineering, University of Washington
2. Dr. Jing Li, Industrial and Systems Engineering, Georgia Institute of Technology
3. Dr. Lauren N. Steimle, Industrial and Systems Engineering, Georgia Institute of Technology
4. Dr. Kai Wang, Computational Science and Engineering, Georgia Institute of Technology
5. Dr. Landon Lempke, School of Medicine, Virginia Commonwealth University

Abstract

Machine learning has shown great promise for improving healthcare through more accurate diagnosis, risk prediction, and personalized care. However, accurate predictions alone do not guarantee better decisions. In practice, healthcare providers must make decisions with limited time, staffing, and resources. Yet these constraints are often overlooked during model development. This dissertation investigates how optimization can be integrated into machine learning to develop decision-making tools that explicitly account for these operational constraints. It addresses three interconnected questions: When is individualized information worth collecting? What information should we prioritize when resources are limited? And who should receive scarce interventions, and when?

The first part of this dissertation examines the value of information in clinical decision-making, developing a framework to quantify how individualized baseline testing changes diagnostic decisions in sports-related concussion assessment. The second part develops an operationally constrained machine learning framework that jointly selects diagnostic assessments and trains interpretable prediction models while accounting for uncertain administration times, clinical knowledge, and operational requirements. The third part introduces a mixed-integer optimization framework for ranked feature selection that optimizes feature rankings simultaneously across multiple resource budgets, accounting for feature acquisition costs and the predictive value of each successive feature set. The final part addresses the dynamic allocation of scarce healthcare resources among pediatric patients whose clinical conditions evolve over time. It proposes a multi-action restless multi-armed bandit framework and a decision-focused learning approach that combines neural networks and long-run resource planning to coordinate clinical interventions under resource constraints.

Together, these contributions advance a shift in healthcare AI from “What can we predict?” to “What actions should we take and for whom, given the resources available?”