AI for Population Health: Melding Data and Algorithms on Networks

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 computational methods which can impact such social challenges. This effort requires new algorithmic and data-driven paradigms which span the full process of gathering costly data, learning models to understand and predict interactions, and optimizing the use of limited resources in interventions. In response to such needs, this thesis presents 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. One example is 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.