Joshua Blumenstock is an Assistant Professor at the U.C. Berkeley School of Information, the Director of the Data-Intensive Development Lab, and the co-Director of the Center for Effective Global Action. 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 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 the 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.
Jacqueline’s research focuses on nonparametric causal methods motivated by real-world policy issues. These methods lean on developments in Machine Learning to create flexible yet robust estimates of causal effects. Jacqueline defended her dissertation for a PhD in Statistics, joint with Public Policy, at Carnegie Mellon University in July 2018. She studied under Edward Kennedy, developing nonparametric causal inference tools to learn about policies to reduce recidivism in Pennsylvania prisons.
Shekhar is a development economist with interests in public economics and political economy related issues in India. He aims to use large-scale government data sets (that have only recently begun to be collected) to better understand government capacity and to combine such data sets with field interventions to address questions of first-order causal interest.
Emily has an undergraduate degree in Computer Science from Harvard, with a secondary field in Global Health. She has previously done research on tracking disease outbreaks and identifying missing people in social media streams. At Berkeley, she is excited to work at the intersection of data science and areas of societal and environmental impact. She’s particularly interested in problems in which machine learning has the potential to transform our current understanding and policies.
Guanhua’s research uses geospatial big data to understand the interaction between the activities of individuals and their geographic context. His areas of focus include social network analysis, human mobility, computational social science, and social media.
Personal website: www.guanghuachi.com
Niall’s research focuses on the intersection of development economics, ICTD, social network analysis, and new methods of data collection in these domains. Niall has 10 years of experience conducting randomized evaluations and primary data collection in developing countries. He holds a BA from the Johns Hopkins University in Economics and International Studies, and an MPA in International Development from the Harvard Kennedy School of Government.
Muhammad Raza Khan is a Ph.D. student in the UC Berkeley Information School. The focus of his research is to use big data to analyze human behavior at both individual and community level. Some examples of his work include customer churn prediction, modeling of product adoption in developing countries and the analysis of gender disparities through large-scale social networks.
Suraj has undergrad and Master’s degrees in development studies from IIT Madras. He has spent the last several years working as a research manager at IFMR, overseeing the implementation and analysis of several large-scale randomized control trials of digital financial services. At Berkeley, he hopes to examine the impact of digital technology and information access on the structures of society and economy, to ensure that digital technologies and services are designed and implemented effectively, in a manner that does not exacerbate existing inequality and exclusions.
Robert has a background in EECS, Statistics and Development Economics and is currently a PhD candidate at the School of Information, Berkeley. He is interested in the application of information technology, causal inference, and machine learning towards poverty reduction with the motto: Try a lot, fail a lot, but measure everything.
Ott is a computational social scientist with a PhD in economics from Aarhus University, Denmark. His research interests are related to social networks, segregation and labor economics, and related statistical methods.
Ofir is a CEGA Data Scientist, conducting data-intensive development research. Prior to joining, he was a mathematical research team leader in an elite technological unit of the Israeli army and then chief Data Scientist and Machine Learning expert for a Tel-Aviv based start-up. He holds a Mathematics & Physics B.Sc. from the Hebrew University in Jerusalem.
Jiaxun is a Data Scientist with an MS in Information Management from UC Berkeley and two Bachelor’s degrees from Tsinghua University in China.
Shikhar is a Data Scientist with Masters Degrees in Computer Science (University at Buffalo) and Development Economics (University of San Francisco). He has previously worked for TechSoup, Amazon, Innovations for Poverty Action, and BRAC.