POSTPONED Machine-Learned Epidemiology: Adam Sadilek, PhD
Thursday, March 26 at 12:00 pm
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1:00 pm
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2020-03-26 19:00:00
2020-03-26 20:00:00
POSTPONED Machine-Learned Epidemiology: Adam Sadilek, PhD
Work in computational epidemiology to date has been limited by coarseness and lack of timeliness of observational data. Most existing models are based on hand-curated statistics that are often delayed, expensive to collect, and cover only limited jurisdictions. Our goal is to lift the state of the art in epidemiology to a new qualitative state, where real-time health predictions become feasible and actionable. We do this at scale by applying federated machine learning and secure aggregation to online data to infer what likely contributed to the contagion. In this talk, I will sample current projects at Google focusing on privacy-first epidemiology research and recent publications (e.g., https://www.nature.com/articles/s41467-019-12809-y, https://www.nature.com/articles/s41746-018-0045-1).
Adam Sadilek focuses on large-scale machine learning applied to health and ecology at Google Research. Before that, he worked on speech understanding at Google[x]. Prior to joining Google, Adam was a co-founder of Fount.in, a machine learning startup providing automated text understanding.
Please join us for our EPPIcenter Seminar Series
About EPPIcenter:
EPPIcenter aims to advance the understanding of infectious diseases to reduce global morbidity and mortality. We believe that the greatest success in the fight against infectious diseases will come through a highly interdisciplinary, systems epidemiology approach, connecting traditionally siloed theoretical work, technology development, generation and collection of empiric data, and analysis using statistical and mathematical modeling. The monthly seminar series will reflect the diversity of disciplines we incorporate into research, training, and tool development, drawing from local and international scientists.
Isabel.Rodriguez@ucsf.edu
America/Los_Angeles
public
Work in computational epidemiology to date has been limited by coarseness and lack of timeliness of observational data. Most existing models are based on hand-curated statistics that are often delayed, expensive to collect, and cover only limited jurisdictions. Our goal is to lift the state of the art in epidemiology to a new qualitative state, where real-time health predictions become feasible and actionable. We do this at scale by applying federated machine learning and secure aggregation to online data to infer what likely contributed to the contagion. In this talk, I will sample current projects at Google focusing on privacy-first epidemiology research and recent publications (e.g., https://www.nature.com/articles/s41467-019-12809-y, https://www.nature.com/articles/s41746-018-0045-1).
Adam Sadilek focuses on large-scale machine learning applied to health and ecology at Google Research. Before that, he worked on speech understanding at Google[x]. Prior to joining Google, Adam was a co-founder of Fount.in, a machine learning startup providing automated text understanding.
Please join us for our EPPIcenter Seminar Series
About EPPIcenter:
EPPIcenter aims to advance the understanding of infectious diseases to reduce global morbidity and mortality. We believe that the greatest success in the fight against infectious diseases will come through a highly interdisciplinary, systems epidemiology approach, connecting traditionally siloed theoretical work, technology development, generation and collection of empiric data, and analysis using statistical and mathematical modeling. The monthly seminar series will reflect the diversity of disciplines we incorporate into research, training, and tool development, drawing from local and international scientists.
