SICSS-Beijing [POSTPONED]

June 14 to June 27, 2020 | Tsinghua University | Beijing, China

beijing

ATTENTION: SICSS-Beijing has been postponed until 2021 due to COVID-19.

From June 14 to June 26Tsinghua University will sponsor a Summer Institute in Computational Social Science, to be held in Beijing, China. The purpose of the Summer Institute is to bring together graduate students, postdoctoral researchers, and beginning faculty interested in computational social science. The Summer Institute is for both social scientists (broadly conceived) and data scientists (broadly conceived).

The instructional program will involve lectures, group problem sets, and participant-led research projects. There will also be outside speakers who conduct computational social science research in a variety of settings, such as academia, industry, and government. Topics covered include text as data, website scraping, digital field experiments, non-probability sampling, mass collaboration, and ethics. There will be ample opportunities for students to discuss their ideas and research with the organizers, other participants, and visiting speakers. Because we are committed to open and reproducible research, all materials created by faculty and students for the Summer Institute will be released open source.

Participation is restricted to Ph.D. students, postdoctoral researchers, and untenured faculty within 7 years of their Ph.D. Most participant costs during the workshop, including housing and most meals, will be covered, and most travel expenses will be reimbursed up to a set cap. We welcome applicants from all backgrounds and fields of study, especially applicants from groups currently under-represented in computational social science. About 20 participants will be invited, and participants are expected to fully attend and participate in the entire two-week program.

Host a Location

You can host a partner location of the Summer Institutes of Computational Social Science (SICSS) at your university, company, NGO, or government agency.

Learn More