Today we continue our ICML series with Elaine Nsoesie, assistant professor at Boston University.
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Elaine presented a keynote talk at the ML for Global Health workshop at ICML 2020, where she shared her research centered around data-driven epidemiology. In our conversation, we discuss the different ways that machine learning applications can be used to address global health issues, including use cases like infectious disease surveillance via hospital parking lot capacity, and tracking search data for changes in health behavior in African countries. We also discuss COVID-19 epidemiology, focusing on the importance of recognizing how the disease is affecting people of different races, ethnicities, and economic backgrounds.
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Connect with Elaine!
- Presentation: Machine Learning and Epidemiology
- Paper: Digital platforms and non-communicable diseases in sub-Saharan Africa
- Paper: Social media captures demographic and regional physical activity
- Paper: Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity
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“More On That Later” by Lee Rosevere licensed under CC By 4.0