AI for Equitable, Data-Driven Decisions in Public Health
Keywords:New Faculty Highlights
AbstractAs exemplified by the COVID-19 pandemic, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. This effort requires new algorithmic and data-driven paradigms which span the full process of gathering costly data, learning models to understand and predict such interactions, and optimizing the use of limited resources in interventions. In response to these needs, I 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. Here, I give an overview of two lines of work.
How to Cite
Wilder, B. (2023). AI for Equitable, Data-Driven Decisions in Public Health. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15459-15459. https://doi.org/10.1609/aaai.v37i13.26826
New Faculty Highlights