SICSS

SICSS
Name	SICSS
Established	2013
Founder	Marco Morales; Christopher Bail
Type	Workshop network
Discipline	Social and computational sciences
Location	Global

SICSS SICSS is an international workshop network bringing together researchers in computational social science, political science, sociology, computer science, and data science for hands-on training, collaboration, and open science. Founded to combine methods from computer science and political science with practices from sociology, statistics, and digital humanities, the program emphasizes reproducibility, team-based projects, and community building across institutions such as Princeton University, Duke University, University of Michigan, Harvard University, and Stanford University.

Overview

SICSS convenes multi-day workshops that integrate tutorials led by faculty from New York University, Massachusetts Institute of Technology, University of Oxford, University of Cambridge, and University of California, Berkeley with mentored project time involving participants from Columbia University, Yale University, University of Chicago, University of Pennsylvania, and Cornell University. Sessions feature tools and platforms from GitHub, R (programming language), Python (programming language), Jupyter Notebook, and Docker (software), and discuss methods exemplified in work published in journals like the American Political Science Review, American Journal of Sociology, Nature Human Behaviour, Science Advances, and Proceedings of the National Academy of Sciences. The workshops also highlight norms from organizations such as the Open Science Framework and standards advocated by the Association for Computing Machinery.

History and Development

SICSS began with leadership drawing on networks linking scholars at Duke University and Princeton University and expanded via local organizers at institutions such as University of Washington, University of Toronto, McGill University, Australian National University, University of Cape Town, and National University of Singapore. Early cohorts included faculty and postdocs affiliated with research centers like the Berkman Klein Center, Miller Center, Center for Information Technology Policy, Data & Society Research Institute, and the Institute for Quantitative Social Science. Conferences and spin-off events intersected with meetings such as the International Communication Association annual conference, the Association for Computational Linguistics workshops, the American Sociological Association meetings, and the Society for Political Methodology summer meeting.

Program Structure and Curriculum

Typical SICSS programs run over a week and combine curricula developed by instructors from Princeton University, Stanford University, Harvard University, University of California, Los Angeles, and Northwestern University. Core modules cover hands-on training in software from TensorFlow, PyTorch, Stata, SPSS, and MATLAB, together with instruction on ethics and regulation involving frameworks like the Common Rule, the General Data Protection Regulation, and guidance from institutional review boards at universities such as Johns Hopkins University and University of California, San Diego. Workshops incorporate case studies referencing influential projects at labs such as Microsoft Research, Google Research, Facebook (now Meta) Research, OpenAI, and Allen Institute for AI.

Research Projects and Outputs

Participants produce collaborative projects that have led to preprints on arXiv, working papers circulated via the Social Science Research Network, and peer-reviewed articles in outlets including the Journal of Political Economy, Quarterly Journal of Economics, Political Analysis, and Journal of Machine Learning Research. Projects have used datasets from sources like the Internet Archive, Twitter, Reddit, Wikipedia, and administrative collections curated by institutions such as the United Nations, European Commission, and World Bank. Methodological outputs often reference techniques from the Hidden Markov Model literature, models like BERT, Latent Dirichlet Allocation, Graph Neural Networks, and algorithms popularized by Geoffrey Hinton, Yann LeCun, and Yoshua Bengio.

Community and Network

SICSS sustains a distributed network of organizers and alumni spanning universities such as Brown University, Vanderbilt University, Rice University, Emory University, Dartmouth College, and University of Texas at Austin. The community coordinates via platforms and societies like the International Machine Learning Society, the Society for Computational Economics, the Data Science Association, and collaborative initiatives linked to projects at Kaggle, Zenodo, and the Harvard Dataverse. Alumni maintain working groups that collaborate on grant proposals to funders such as the National Science Foundation, the European Research Council, the National Institutes of Health, Wellcome Trust, and private foundations like the Gordon and Betty Moore Foundation.

Impact and Recognition

SICSS has been cited for expanding capacity at institutions in regions including Latin America (e.g., Universidad de Buenos Aires), Africa (e.g., University of Cape Town), Asia (e.g., Tsinghua University), and Oceania (e.g., University of Melbourne). Recognition includes invitations to speak at venues like the United Nations General Assembly side events, panels at the Computers, Privacy and Data Protection Conference, and contributions to policy discussions at the European Commission and Organisation for Economic Co-operation and Development forums. Program alumni have gone on to faculty positions and leadership roles at organizations such as Amazon Research, IBM Research, RAND Corporation, and national statistical offices including the United States Census Bureau and Statistics Canada.

Participation and Application Process

Local organizers at institutions including University of Illinois Urbana-Champaign, Pennsylvania State University, University of Wisconsin–Madison, Georgia Institute of Technology, and Purdue University advertise calls for applicants and manage selection processes that weigh academic records from applicants affiliated with programs such as the PhD program at Harvard, Oxford DPhil, Stanford PhD, and postdoctoral fellowships at centers including the Russell Sage Foundation and National Bureau of Economic Research. Application materials typically require a CV, statement of interest, and coding sample; finalists are selected by committees that may include faculty from Princeton, Duke, Yale, and UC Berkeley and receive announcements via mailing lists maintained by networks such as the Computational Social Science Society of the Americas.

Category:Workshops