Using Big Data to Fight Poverty

Track 1

Tuesday, April 26, 2022 - 2:30 pm to 3:00 pm

In wealthy nations, big data and artificial intelligence are creating exciting opportunities for commercial profit and academic research. In developing economies, however, data is much scarcer, and it remains unclear if and how the world’s poor will benefit from the “data revolution.” This talk will discuss ongoing work that leverages innovations in machine learning to tackle problems affecting poor and marginalized populations, and will also highlight some challenges and pitfalls to be wary of in this line of research.

Joshua Blumenstock

Associate Professor

UC Berkeley School of Information

Joshua Blumenstock is an associate professor at the UC Berkeley School of Information and the director of the Data-Intensive Development Lab. His research lies at the intersection of machine learning and development economics, and focuses on using novel data and methods to better understand the causes and consequences of global poverty. At Berkeley, Joshua teaches courses in machine learning and data-intensive development.

He has a Ph.D. in information and an M.A. in economics from UC Berkeley and bachelor’s degrees in computer science and physics from Wesleyan University. He is a recipient of the Intel Faculty Early Career Honor, a Gates Millennium Grand Challenge award, and a Google Faculty Research Award, and the U.C. Berkeley Chancellor's Award for Public Service. His work has appeared in a variety of publications including Science, Nature, the American Economic Review, and the proceedings of KDD and AAAI.