Important updates -- April 17-24, 2020, from BAY-SICSS:
- BAY-SICSS 2020 will be presented virtually.
- New dates (total: 3-week program): June 15 through July 3rd
- New application deadline: extended to FRIDAY, MAY 1st.
- Details are in the process of being updated on the BAY-SICSS website.
- For more information, please contact email@example.com.
On June 15 - July 3, 2020, Stanford University and the University of California, Berkeley, will co-host the San Francisco Bay Area Summer Institute in Computational Social Science (BAY-SICSS). The event will be held virtually with a fully online participation format.
The purpose of the Summer Institute is to bring together graduate students, postdoctoral researchers, and beginning faculty interested in computational social science in the public interest. Participation is restricted to PhD students, postdoctoral researchers, and untenured faculty within 7 years of their PhD. The event will have a thematic focus on computational social science in partnership with Bay Area nonprofits.
Program: The first week's instructional program will involve lectures, group problem sets, and participant-led research projects, held concurrently with SICSS-Duke. During the second part of the program, participants will co-develop a two-week, part-time project with Bay Area nonprofits in health, criminal justice, and civil society.
New Application Deadline:
APPLY NOW - Application materials are due Friday, May 1, 2020.
Sponsors: This event is being supported by Stanford University (the Institute for Research in the Social Sciences, the Human-Centered AI Initiative, and the School of Humanities and Sciences) and UC Berkeley (the Berkeley Institute for Data Science and D-Lab), and Hopelab.
David J. Harding is Professor of Sociology and Faculty Director of the D-Lab, which supports data-intensive research in the social sciences, humanities, and beyond. He studies poverty and inequality, urban neighborhoods, education, culture, and the criminal justice system. Harding’s methodological interests include causal inference and the integration of qualitative and statistical methods.