Title: Addressing Problems in Global Health with Non-traditional Data and Machine Learning
Abstract: In this talk, I will present examples of my work on using non-traditional data from satellite images, social media, and other Internet sources to address global health problems. I will also discuss the challenges associated with using data from these sources. To conduct effective research using these data, it is important to consider and incorporate into analytical processes the distinct social, cultural, and economic context in different countries and communities.
Dr. Nsoesie applies data science methodologies to global health problems, using digital data and technology to improve health, particularly in the realm of surveillance of chronic and infectious diseases. She has also been appointed as a BU Data Science Faculty Fellow, as part of the BU Data Science Initiative at the Hariri Institute for Computing. The Data Science Faculty Fellows program assembles a cluster of uniquely talented faculty whose expertise transcends traditional disciplinary boundaries to enable fundamental advances in data science. Dr. Nsoesie completed her PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech, and her PhD dissertation, Sensitivity Analysis and Forecasting in Network Epidemiology Models, at the Network Dynamics and Simulations Science Lab at Virginia Tech BioComplexity Institute. After postdoctoral associate positions at Harvard Medical School and Boston Children’s Hospital, Dr. Nsoesie joined the faculty of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.
Recording of the talk: https://youtu.be/AA5lEDXCbgc
LOCATION: STATUS:CONFIRMED DTSTART:20201019T180000Z DTEND:20201019T180000Z END:VEVENT END:VCALENDAR