Prescriptive Analytics and Data-Driven Optimization Paradigms for Enhanced Operations
Abstract:
In the first part of the talk, we present prescriptive analytics paradigms for data-driven decision-making for complex systems and environments. Conventional data-driven decision-making methods assume access to plentiful, well-structured data to generate a prediction model, and subsequently, an optimization model is used to create a decision. However, real data can be incomplete (sparse, missing), partially observable, time-varying, and unstructured. We present joint learning and optimization frameworks to tackle emerging challenges in services and operations caused by the complexity and limitations of data and difficulties inherent in data-system integration. Our approaches are based on amalgams of machine learning, distributionally robust/stochastic optimization, and combinatorial optimization. In the second part of the talk, we discuss the application of data-driven optimization to kidney exchange.
April 9, 2024 (Tuesday)
10:00-11:00AM
Zoom link
https://wpi.zoom.us/j/8389374249
Hoda Bidkhori is currently an assistant professor in the Department of Computational and Data Sciences at George Mason University. Her research focuses on the theory and applications of data analytics and data-driven decision-making; the application areas include logistics, supply chain management, and healthcare. Before, she was an assistant professor in the Department of Industrial Engineering at the University of Pittsburgh. Prior to this, she was a lecturer at the Operations Research and Statistics Group at the MIT Sloan School of Management and a postdoctoral research associate in Operations Research at MIT. She holds a PhD degree in Applied Mathematics from MIT.
Contact: hbidkhor@gmu.edu