AI for Population Health: Melding Data and Algorithms on Networks

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Passcode:  222222

Abstract:  As exemplified by the COVID-19 pandemic, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. My research aims to build AI which can impact such social challenges, advancing health and equity on a population level. This effort requires new algorithmic and data-driven paradigms which span the full process of gathering costly data, developing machine learning models to understand and predict interactions, and optimizing the use of limited resources in interventions. In response to these needs, I will present methodological developments at the intersection of machine learning, optimization, and social networks which are motivated by on-the-ground collaborations on HIV prevention, tuberculosis treatment, and the COVID-19 response. These projects have produced deployed applications and policy impact. For example, I will present the development of an AI-augmented intervention for HIV prevention among homeless youth. This system was evaluated in a field test enrolling over 700 youth and found to significantly reduce key risk behaviors for HIV.

Bio:  Bryan Wilder is a PhD student in Computer Science at Harvard University, where he is advised by Milind Tambe. His research focuses on the intersection of optimization, machine learning, and social networks, motivated by applications to population health. His work has received or been nominated for best paper awards at ICML and AAMAS, and also received second place in the INFORMS Doing Good with Good OR competition. He is supported by the Siebel Scholars program and previously received a NSF Graduate Research Fellowship.