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Decision-Making Models in Healthcare
Decision-Making Models in Healthcare
Semester(s): Fall 2024
Semester(s): Fall 2024
Level: Graduate
Level: Graduate
This course provides an overview of analytical/mathematical methods that can help make better decisions in the healthcare ecosystem. The class will focus on a combination of methodologies including stochastic optimization and Markov models, and their applications in (1) medical decision making, (2) healthcare operations, and (3) public health and policy. Students will learn how to formulate and solve decision-making models arising in healthcare settings, particularly in the presence of uncertainty. I will primarily focus on modeling frameworks from operations research community that are frequently used in clinical decision making, healthcare scheduling and logistics, and public health policy making. While the modeling frameworks and techniques are discussed within healthcare applications, the students can apply the same frameworks and techniques to other disciplines/applications of their interest.
Network Optimization
Network Optimization
Semester(s): Spring 2024, Spring 2025
Semester(s): Spring 2024, Spring 2025
Level: Graduate
Level: Graduate
In this graduate-level course, I aim to teach students how to formulate and solve decision-making models with an underlying network structure; we further explore decision-making problems with no resemblance to networks. We will start with basics of mathematical optimization theory and computational complexity, and discuss why some network optimization problems are amenable to efficient solution techniques, while others are not. We will utilize optimization theory and elements of discrete mathematics to develop fast solution techniques for several class of network models, including minimum cost flow, traveling salesman problem, vehicle routing problems, and convex flow problems.
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