Healthcare Analytics with Bandits and Transfer Learning
Overview:
Small data problems frequently arise in healthcare due to imbalanced or delayed outcomes, making statistical inference challenging. A promising approach to addressing these issues is to leverage adaptive data collection (bandits) in tandem with auxiliary data sources (transfer learning). This seminar provides an overview of recent techniques and algorithms with applications to personalized interventions, clinical trial designs with surrogates, as well as a large-scale targeted COVID-19 screening system in Greece.
Speaker:
Hamsa Bastani, PhD, is an assistant professor in Operations Information and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, pricing, recommendation systems, and social good. Her work has been recognized by the George Nicholson, MSOM, Service Science, and Health Applications Society best student paper awards, the Pierskalla best paper award in healthcare operations, and the early-career People’s Choice award in sustainable operations. She previously completed her PhD at Stanford University, and was a Herman Goldstine postdoctoral fellow at IBM Research.
Originally recorded on Wednesday, May 19, 2021, as part of CHOIR's
Overview:
Small data problems frequently arise in healthcare due to imbalanced or delayed outcomes, making statistical inference challenging. A promising approach to addressing these issues is to leverage adaptive data collection (bandits) in tandem with auxiliary data sources (transfer learning). This seminar provides an overview of recent techniques and algorithms with applications to personalized interventions, clinical trial designs with surrogates, as well as a large-scale targeted COVID-19 screening system in Greece.
Speaker:
Hamsa Bastani, PhD, is an assistant professor in Operations Information and Decisions at the Wharton School, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications to healthcare operations, pricing, recommendation systems, and social good. Her work has been recognized by the George Nicholson, MSOM, Service Science, and Health Applications Society best student paper awards, the Pierskalla best paper award in healthcare operations, and the early-career People’s Choice award in sustainable operations. She previously completed her PhD at Stanford University, and was a Herman Goldstine postdoctoral fellow at IBM Research.