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Name: Kai Wang, Ph.D. Candidate at Harvard University
Date: Tuesday, February 16, 2023 at 11:00 am
Location: Coda 230
Link: This seminar is an in-person event only. However, the seminar will be recorded and uploaded to the School of Computational Science and Engineering channel on Georgia Tech MediaSpace following the presentation.
Title: Integrating Machine Learning and Optimization with Applications in Public Health and Sustainability
Abstract: This talk summarizes the importance of integrating optimization in both offline and online learning with applications in public health and environmental sustainability. Existing machine learning approaches primarily focus on training predictive models separately from optimization, which leads to a mismatch in predictive performance and decision quality in the downstream optimization tasks. This talk covers my work on decision-focused learning to integrate feedback from optimization to train predictive models, to avoid this mismatch. My work provides the first decision-focused learning algorithm for sequential decision problems and it significantly reduces the computation cost to enable applications in large-scale public health problems. My decision-focused learning algorithm is currently deployed in a maternal and child health program used by 100,000 beneficiaries in India to effectively schedule limited health workers to improve mothers’ engagement with health information.
Bio: Kai Wang is a Ph.D. candidate in Computer Science at Harvard University, advised by Professor Milind Tambe. Kai's research interests include multi-agent systems, computational game theory, machine learning and optimization, and their applications in public health and conservation. One of Kai's key technical contributions includes decision-focused learning, which integrates machine learning and optimization to strengthen learning performance; with his algorithms currently deployed assisting a non-profit in India focused on improving maternal and child health. Kai is honored to be the recipient of the Siebel Scholars award and the best paper runner-up award at AAAI.
