Addressing Problems in Global Health with Non-traditional Data and Machine Learning

Abstract

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. In order to conduct effective research using these data sources, it is important to consider and incorporate into analytical processes the distinct social, cultural, and economic context in different countries and communities.

BIO:

Dr. Nsoesie is an Assistant Professor of Global Health at Boston University School of Public Health. She has a PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech. She also has an MS in Statistics and a BS in Mathematics. Her research is focused on the use of digital data and technology to improve health in communities globally. Her work has also focused on addressing bias in digital data and understanding factors influencing disparities in health outcomes. Her research findings have been reported in major news outlets and science magazines, including Science, Smithsonian Magazine, Scientific American, Washington Post, NPR, and the BBC. She is on the advisory board of Data Science Africa and Data Science Nigeria. She is also the founder of Rethé - an initiative focused on providing scientific writing tools and resources to student communities in Africa in order to increase representation in scientific publications. She has written for NPR, The Conversation, Public Health Post, and Think Global Health. Dr. Nsoesie was born and raised in Cameroon.

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