A presentation by Joshua Blumenstock, Assistant Professor, Information School, University of Washington
Description:
A presentation by Joshua Blumenstock, Assistant Professor, Information School, University of Washington
Date: Thursday, February 5, 2015
Time: 4:00 – 5:00 p.m.
Location: Donald Bren Hall Room 4011 (directions)
RSVP is not required
Abstract:
In recent years, the rapid proliferation of mobile phones in developing countries has provided billions of individuals with novel opportunities for social and economic interaction. Concurrently, the data generated by mobile phone networks is enabling new data-intensive methods for studying the social and economic behavior of individuals in resource-constrained environments. After all, these data reflect much more than simple communications activity: they capture the structure of social networks, decisions about expenditures and consumption, patterns of travel and mobility, and the regularity of daily routines. In this talk, I will discuss the results from two recent projects that derive behavioral insights from mobile phone data. The first study uses data on Mobile Money transfers in Rwanda and microeconomic models to better understand the motives that cause people to send money to friends and family in times of need. The second project combines call data with follow-up phone surveys to investigate the extent to which it is possible to predict an individual’s wealth and happiness based on his or her prior history of phone calls and several supervised learning models. These projects are enabled by generous support from the Institute for Money, Technology, and Financial Inclusion; Intel; the Gates Foundation; and the NSF.
Bio:
Joshua Blumenstock is an Assistant Professor at the Information School, an Adjunct Assistant Professor of Computer Science and Engineering, and a founder of the Data Science and Analytics Lab at the University of Washington. His research develops theory and methods for the analysis of large-scale behavioral data, with a focus on how such data can be used to better understand poverty and economic development. Recent projects combine field experiments with big spatiotemporal network data to model decision-making in poor and conflict-affected regions of the world. Prior to joining UW, Joshua was a postdoc in the Department of Economics at Yale University. 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 and a former fellow of the Thomas J. Watson Foundation and the Harvard Institutes of Medicine.
