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Workshop
Tue Dec 14 05:55 AM -- 12:40 PM (PST)
Machine Learning in Public Health
Rumi Chunara · Daniel Lizotte · Laura Rosella · Esra Suel · Marie Charpignon





Workshop Home Page

Public health and population health refer to the study of daily life factors, prevention efforts, and their effects on the health of populations. Building on the success of our first workshop at NeurIPS 2020, this workshop will focus on data and algorithms related to the non-medical conditions that shape our health including structural, lifestyle, policy, social, behavior and environmental factors. Data that is traditionally used in machine learning and health problems are really about our interactions with the health care system, and this workshop aims to balance this with machine learning work using data on non-medical conditions. This year we also broaden and integrate discussion on machine learning in the closely related area of urban planning, which is concerned with the technical and political processes regarding the development and design of land use. This includes the built environment, including air, water, and the infrastructure passing into and out of urban areas, such as transportation, communications, distribution networks, sanitation, protection and use of the environment, including their accessibility and equity. We make this extension this year due to the fundamentally and increasingly relevant intertwined nature of human health and environment, as well as the recent emergence of more modern data analytic tools in the urban planning realm. Public and population health, and urban planning are at the heart of structural approaches to counteract inequality and build pluralistic futures that improve the health and well-being of populations.

Welcoming Remarks (Live)
Keynote #1 Dr. Subhrajit "Subhro" Guhathakurta (Talk)
Keynote #1 Dr. Guhathakurta Live Q&A (Live Q&A)
Deep Learning for Spatiotemporal Modeling of Urbanization (Contributed talk 1)
Deep Learning for Spatiotemporal Modeling of Urbanization (Contributed talk 1 Q&A)
Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes (Contributed talk 2)
Restless Bandits in the Field: Real-World Study for Improving Maternal and Child Health Outcomes (Contributed talk 2 Q&A)
A Brief Summary on Covid-19 Pandemic & Machine Learning Approaches (Lightning talk 1)
Assisted Living in the United States: an Open Dataset (Lightning talk 2)
A probabilistic approach to evaluating Cryptosporidium health risk in drinking water (Lightning talk 3)
Keynote #2 Dr. Andrea Parker (Talk)
Keynote #2 Dr. Parker Live Q&A (Live Q&A)
Demand prediction of mobile clinics using public data (Contributed talk 3)
Demand prediction of mobile clinics using public data (Live Q&A)
Role of Attachment Variables in Resilient Families (Contributed talk 5)
Role of Attachment Variables in Resilient Families (Live Q&A)
Reconciling Risk Allocation and Prevalence Estimation in Public Health Using Batched Bandits (Lightning talk 4)
An mHealth Intervention for African American and Hispanic Adults: Preliminary Results from a One-Year Field Test (Lightning talk 5)
Kronecker Factorization for Preventing Catastrophic Forgetting (Lightning talk 6)
Keynote #3 Dr. Sanjay Basu (Talk)
Learning after Deployment: The Missed Tale of Supervision (Lightning talk 7)
Contrastive Learning for PM2.5 Prediction from Satellite Imagery (Lightning talk 8)