In wealthy nations, improved algorithms and new sources of “big data” are creating exciting opportunities for commercial profit and academic research. In developing economies, however, fewer sources of robust data exist, and it remains unclear if and how the world’s poor will benefit from new approaches to data science. In this talk, Dr. Blumenstock will discuss ongoing work that capitalizes on recent advances in machine learning to tackle some of the problems affecting poor and marginalized populations. This talk will focus on recent results from Afghanistan, Indonesia, and Rwanda, which illustrate how large-scale data from mobile phone and satellite networks can be combined with on-the-ground experiments and surveys to better understand the causes and consequences of global poverty. Throughout, I will highlight open technical research questions where the right computer scientist or statistician has the opportunity to make a lasting real-world impact.
The Berkeley Distinguished Lectures in Data Science, co-hosted by the Berkeley Institute for Data Science (BIDS) and the Berkeley Division of Data Sciences, features faculty doing visionary research that illustrates the character of the ongoing data, computational, inferential revolution. In this inaugural Fall 2017 "local edition," we bring forward Berkeley faculty working in these areas as part of enriching the active connections among colleagues campus-wide. All campus community members are welcome and encouraged to attend. Arrive at 3:30pm for tea, coffee, and discussion.
Joshua Blumenstock is an Assistant Professor at the U.C. Berkeley School of Information, and the Director of the Data-Intensive Development Lab. His research focuses on developing new methods for using massive, spatiotemporal network data to better understand poverty and economic development. At Berkeley, Joshua teaches courses in machine learning and data-intensive development. Previously, Joshua was on the faculty at the University of Washington, where he founded and co-directed the Data Science and Analytics Lab, and led the school’s Data for Social Good initiative. He has a Ph.D. in Information Science and a M.A. in Economics from U.C. 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, a Google Faculty Research Award, and a former fellow of the Thomas J. Watson Foundation and the Harvard Institutes of Medicine.