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PhD in Operations Research for Healthcare Systems Optimization

University of Oklahoma
Self-funded 🎓 Computer Science 🎓 Mathematics machine learning optimization operations research healthcare stochastic optimization medical decision making data-driven modeling healthcare delivery

Explore advanced mathematical and computational methods to solve complex healthcare challenges. Join Dr. Hemmati's lab to use operations research and machine learning for improving medical decision processes and patient care.

AI-generated overview

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Why This Research Matters

This research addresses critical challenges faced by healthcare systems, aiming to improve resource allocation and patient outcomes through data-informed decision making. The integration of operations research and machine learning techniques can transform healthcare delivery and support more effective, efficient medical practices.

Medical Decision Making Stochastic Optimization Bilevel Optimization Interdiction Games

Project Description

Project Overview

This PhD research focuses on addressing health-related decision-making problems in healthcare systems using advanced mathematical and computational methods. Emphasis is placed on stochastic optimization, machine learning, and data-driven modeling to tackle complexities in healthcare delivery.

What You Will Do

The candidate will conduct research on healthcare operations and medical decision processes within Dr. Hemmati's lab. Projects include modeling uncertainties in healthcare, optimizing treatment protocols, and developing predictive patient care models using large-scale data analytics and machine learning.

Expected Outcomes

The research aims to optimize resource allocation, improve patient outcomes, and enhance the overall effectiveness and efficiency of healthcare delivery by applying operations research and computational science techniques to real-world healthcare problems.

Why This Matters

Healthcare systems today face increasing demands for efficiency and data-informed decision making. Combining operations research and machine learning addresses these needs, with potential high societal impact by transforming medical decision processes and contributing valuable advancements to applied mathematics and computational sciences.

Entry Requirements

Thesis-based master’s degree in operations research, mathematics, industrial engineering, computer science, or related field; prior research experience with at least one relevant publication; proficiency in mathematical modeling, optimization, and/or machine learning; strong analytical and communication skills.

How to Apply

Prepare a resume highlighting academic and research achievements. For detailed application instructions, refer to the official LinkedIn post: https://www.linkedin.com/posts/soheil-hemmati-06715664_m-soheil-hemmati-share-7452378179362521088-UAWy

Eligibility

UK/Home
EU
International

Supervisor Profile

DS
Dr. Soheil Hemmati
University of Oklahoma
Google Scholar

Dr. Soheil Hemmati leads research focused on innovative healthcare operations and medical decision-making processes, utilizing stochastic optimization, machine learning, and large-scale data analytics. His work integrates mathematical and computational approaches to deliver solutions improving healthcare management. He is recognized for interdisciplinary research bridging operations research and healthcare systems.

Key Publications

2023 63 citations
Risk model–based lung cancer screening: a cost-effectiveness analysis
2016 49 citations
A mixed-integer bilevel programming approach for a competitive prioritized set covering problem
2014 32 citations
A cutting-plane algorithm for solving a weighted influence interdiction problem
2022 29 citations
MR-guided adaptive radiotherapy for OAR sparing in head and neck cancers
2024 16 citations
Cluster-based toxicity estimation of osteoradionecrosis via unsupervised machine learning: Moving beyond single dose-parameter normal tissue complication probability by using …

Research Contributions

Developed models and algorithms for bilevel and stochastic optimization problems applied to health and industrial engineering.
These contributions improve decision-making and resource allocation in healthcare and complex systems.
Applied adaptive radiotherapy techniques and toxicity estimation methods to improve treatment outcomes for head and neck cancers.
These advances potentially reduce treatment side effects and personalize cancer therapy.
Utilized risk-based modeling and cost-effectiveness analysis for lung cancer screening programs.
This work informs optimal screening strategies to enhance public health outcomes.

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