Are you interested in exploring the role of machine learning in disciplines concerned with the social, environmental and political processes of counteracting inequality and building pluralistic futures? 

Consider submitting your work to and/or attending this year's Machine Learning in Public Health workshop at NeurIPS. This year we 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 are excited for the machine learning community to join and make an impact in this important area.

We expect contributions on, but not limited to the following areas:

· Data: feature generation from non-clinical, e.g. internet/mobile datasets relevant to health, privacy and security challenges related to public health and urban planning data and tasks, ascertainment of data to measure and define factors related to social disparities

· Methods: methods for combining non-clinical and clinical data for public and population health applications, algorithms for public health and urban planning goals, model transport across environments, spatial analyses

· Policy and implementation: ML approaches for mitigating disparities, identifying methodological assumptions that fail in public health and urban planning settings, human and ML interaction in the public health and urban planning context

· Health Topics: ML integration in infectious disease models, improving non-communicable disease surveillance and prediction using ML, health equity