Elaine O. Nsoesie is an Associate Professor in the Department of Global Health at the Boston University School of Public Health. She is an internationally recognized data scientist and a leading voice on the use of data and technology to advance health equity. She is a Data Science Faculty Fellow and was a Founding Faculty of the Boston University Faculty of Computing and Data Sciences.

She served as a program lead and senior advisor to the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program at the National Institutes of Health through the Intergovernmental Personnel Act (IPA) Mobility Program. She also led the Racial Data Tracker project at the Boston University Center for Antiracist Research.

She has expertise in the application of data science methods (including, machine learning and artificial intelligence) and data from non-traditional public health sources (such as, mobile phones, satellites, and social media) to address major global health challenges. Her work approaches health equity from multiple angles, including increasing representation of communities typically underrepresented in data science through programs like Data Science Africa and AIM-AHEAD; addressing bias in health data and algorithms; and using data and policy to advance racial equity. She has collaborated with local departments of health in the U.S. to improve disease surveillance systems, international organizations like UNICEF and UNDP, and served as a Data & Innovation Fellow in the Directorate of Science, Technology, and Innovation (DSTI), The President’s Office, Sierra Leone.

She has published extensively in peer-reviewed literature. She is also known for her ability to effectively communicate complex information with diverse audiences. She has given more than 100 invited talks including keynotes and distinguished lectures at conferences, workshops and institutions across five continents.

Nsoesie was born and raised in Cameroon.

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, she joined the faculty of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.