The COVID-19 pandemic has cast a spotlight on the importance of public health. Even beyond this current emergency, public health is an essential component of population-level wellbeing. Topics such as infectious disease surveillance and control, preventative health, behavioral and mental health, maternal and child wellbeing, and more all play a crucial role in society. Moreover, a range of applications in public health benefit from careful use of data to uncover outbreak dynamics, learn patterns of behavior, optimize the design of interventions, and more. The science of machine learning in a public health context is still rapidly developing, and our aim is to build a community encompassing researchers based in both machine learning and public health to address these shared questions.

Examples of relevant topics for the workshop include (but are not limited to):

These topics are intended to provide examples of questions in line with the workshop, not to provide an exhaustive list. We solicit papers across the full range from computation to applications from researchers in either field who are interested in engaging with an interdisciplinary community.

Papers should be at most 4 pages in length, in ICLR format. We welcome research which is in progress, submitted, or recently published (submissions already accepted in an archival venue should provide a link to the full paper). The workshop will not have an archival proceedings, but accepted papers will be posted on the website. We also welcome position papers which propose new questions or argue for a new perspective on this emerging field. Submissions will be considered for both posters and full-length contributed talks. A link to the submission site will be available on this page closer to the deadline.