Computational Social Science Training Program (CSSTP)

The UC Berkeley Computational Social Science Training Program (CSSTP) trains predoctoral students representing a variety of degree programs and expertise areas in the social sciences, including demography, public health, public policy, social epidemiology, social welfare, and sociology. 

Launched in 2020 with a five-year, $1.2 million grant from the National Institutes of Health (NIH) Office of Behavioral and Social Sciences (OBSSR) and its partner institute, the Eunice Kennedy Shriver National Institute of Child Health and Human Development, this two-year multidisciplinary training program in advanced data analytics supports predoctoral students focusing on the social and behavioral sciences. Fellows participating in this program develop advanced computational and data science analytics skills to address urgent needs in biomedical, behavioral, social and clinical research. They are being trained to take advantage of recent advances in medical informatics, electronic health records, big data analytics, mobile/wearable technologies, social media and web-generated data, as well as geospatial and administrative data.

This program is a collaborative effort currently led by David J. Harding, professor of Sociology and Faculty Director of the Berkeley Social Science Data Laboratory (D-Lab);  and Patrick Bradshaw, professor of epidemiology of the UC Berkeley School of Public Health; and with support from BIDS colleagues Kirstie Whitaker, Lilli Wessling Hart, and Jamilah Karah. Special thanks to Heather A. Haveman, professor of Sociology and Business, for her contributions to the program.

Get full details about this program’s implementation and training faculty below. Please contact css-t32@berkeley.edu with any questions.

View this series' 2020-2022 videos in the CSS Training Program video archive.

Application Requirements

Applicants must:

  • Be second- or third-year PhD students in Fall 2026
  • Be enrolled in doctoral programs in Sociology, Demography, Epidemiology, Public Health Policy, Social Welfare, or Public Policy.
  • Have completed first-year course requirements in their home department by the start of the fellowship
  • Be US citizens or permanent residents

Submit your application here - deadline: April 5, 2026, 11:59 pm PT 

Questions about CSS? Please contact us: css-t32@berkeley.edu

Funding

During their first year in the program, trainees receive:

  • 1 year of fellowship support

  • Funding includes $28,788 stipend and 60% of tuition and fees for fall and spring semesters.

  • Trainees’ home departments are responsible for the difference between the CSSTP stipend and GSR trainee salary minimums, as well as the remaining 40% of tuition and fees and student health insurance.

  • Up to $3,000 in childcare costs for eligible trainees

Due to NIH regulations, trainees are prohibited from working more than 25% time during the first-year appointment.

CRELS and CSS Training Program Opportunities

The CSS and CRELS (Computational Research for Equity in Legal Systems) training programs are distinct efforts, funded by two separate training grants. However, we manage many activities as one cohort, maximizing trainees’ opportunities for collaboration and to learn from each other.

Combined activities

CRELS and CSS trainees benefit from:

  • Faculty and program mentorship through guidance from faculty and program leadership, including support on research design, professional growth, and navigating interdisciplinary projects
  • Weekly research workshop where trainees share current research, receive feedback from peers and faculty, and engage in discussion around methods, research design, and career development
  • Invited faculty presentations as part of the CRELS sponsored speaker series (speakers selected with cohort input), along with opportunities to meet with the guest speakers in small group or one-on-one settings
  • Science communication workshop, a multi-day training focused on communicating research to broader audiences, including sessions with journalists, public communicators, and experienced writers
  • Access to the Berkeley Institute for Data Science (BIDS) community, including talks, workshops, hackathons, and open source community events such as those connected to Berkeley’s Open Source Program Office (OSPO), Cultural Analytics Group, as well as collaborative spaces like the AI Futures Lab (AIFL)

CSS Training Program Goals

CSSTP emphasizes a team science approach to problem solving and prepares students to apply novel methodologies and data analytic techniques in behavioral and social sciences research. Trainees can expect to acquire the following core competencies during the two years of the program:

  • Methods and tools for causal inference with observational data, particularly longitudinal data with time-varying confounding, adaptive interventions, and mediation analysis with longitudinal data;
  • Unsupervised and supervised machine learning algorithms and their application to health data, particularly the relationship between conventional regression models and supervised machine learning, and use of non-standard loss functions to harness supervised machine learning for problems of particular relevance in the social sciences (such as identification and response to effect heterogeneity);
  • Principles, methods, and applications of text analysis and natural language processing;
  • Expertise in responsible conduct of research, including tools for reproducibility in research and best practices for transparency and open science;
  • Proficiency in tools and methods for research with high volume, high intensity, and non-rectangular data, including data manipulation and transformation, cloud computing, and data security;
  • Effective written and oral communication in preparation for article writing, grant writing, presentation of research results, and teaching.

The program accommodates the requirements of each of the constituent PhD programs while providing sufficient flexibility to explore specific interests through an individualized training plan. This built-in flexibility and careful sequencing of required elements ensures that trainees’ time to degree is not delayed.

This diagram illustrates the program core competencies and program design:

Core Competencies and Program Design illustration

Program Design

Primary components of CSS training program include:

  1. A new year-long course in computational social science that prepares trainees to employ advanced data analytic methods;
  2. Two data science elective courses selected from existing courses to develop deep specialized knowledge and skills (see list below);
  3. Data science research internships in UC Berkeley faculty labs and/or industrial labs;
  4. A weekly Computational Social Science Workshop (CSS Workshop) where faculty and trainees give/receive peer mentoring, focus on professional development, discuss new research articles, are supported in paper writing and article submission, and receive feedback on ongoing research;
  5. Participation in regular professional development activities, including the Computational Social Science Annual Meeting (CSS Annual Meeting) to present preliminary work and receive feedback, and data science conferences to share research and begin building a professional network;
  6. Responsible Conduct of Research training, involving both principles and tools and emphasizing reproducibility in research, at multiple points in the training program and integrated into other primary components, including the CSS core course, CSS Workshop, and internship.
  7. Joint mentorship by both social science and data science training faculty;
  8. A research focus on intensive or voluminous longitudinal data and data from high-density, large sample or population level agency databases.

Trainees who successfully complete the 2-semester CSSTP Core Course, and in addition INFO 201 (Research Design and Applications for Data & Analysis), will be eligible to receive the Graduate Certificate in Applied Data Science that documents trainee competencies in advanced data analytics and distinguishes them from other social science PhDs and makes them more competitive for postdoctoral and other positions after graduation.

Year 2 of grad studies; Program Year 1: Close to the beginning of fall instruction, trainees attend a program orientation that covers program requirements and structure, advice on connecting with data science faculty, requirements for responsible conduct of research training, review of individual development plans, and an introduction to key program leadership and training faculty. Initially, each trainee is matched with one social science mentor and one data science mentor based on their research interests and developmental goals, although trainees are allowed if necessary to change mentors as the program progresses. Students who may need extra preparation and support for success in CSSTP are counseled by the co-Directors on necessary BIDS or D-Lab training. During Program Year 1, trainees take the two semester CSS Core Course, attend the weekly CSS Forum, RCR training, and the CSS Annual Meeting, and the BSSR Data Analytics T32 cross-site grantee meeting in Washington, DC.

Year 3 of grad studies; Program Year 2. Trainees engage in a two-semester internship in a data science lab/research group on campus or externally in industry, government, or non-profit partners, and complete two elective courses in data science. They also continue their involvement in the CSS Forum and CSS Annual Meeting. A central goal of this year in CSSTP will be ensuring all trainees submit at least one paper for publication by the end of the year.

This diagram further illustrates the program design:

Program Connections illustration

Examples of electives for CSSTP

Program Directors and Training Faculty

The CSS Training Program co-Directors have varied and highly complementary expertise in biomedical training, curriculum development, project management, and diversity/inclusion. David J. Harding is Professor of Sociology, Faculty Director of the Social Sciences D-Lab, and a BIDS Senior Fellow. Patrick Bradshaw is Associate Professor of Epidemiology and Endowed Chair of Martin Sisters Medical Research & Public Health. Jennifer Ahern is Associate Dean for Research and Professor of Epidemiology at the School of Public Health. In addition, CSSTP Training Faculty are available to mentor CSSTP Fellows on specific research projects.

Training Faculty from the Social Sciences

  • Adrian Aguilera, Social Welfare
  • Jennifer Ahern, Epidemiology
  • Jill Berrick Social, Welfare
  • Stefano Bertozzi, Health Policy
  • Henry Brady, Public Policy
  • Amanda Brewster, Health Policy
  • Yu-Ling Chang, Social Welfare
  • Julian Chow, Social Welfare
  • Emmeline Chuang, Social Welfare
  • William Dow, Health Policy
  • Dennis Feehan, Demography
  • Lia Fernald, Health Policy
  • Josh Goldstein, Demography
  • David Harding, Sociology
  • Hilary Hoynes, Public Policy
  • Rucker Johnson, Public Policy
  • Jenna Johnson-Hanks, Sociology and Demography
  • Erin Kerrison, Social Welfare
  • Danya Lagos, Sociology
  • Barbara Laraia, Epidemiology
  • Ayesha Mahmud, Demography
  • Anu Manchikanti Gómez, Social Welfare
  • Mahasin Mujahid, Epidemiology
  • Ziad Obermeyer, Health Policy
  • Emily Ozer, Epidemiology
  • Caitlin Patler, Public Policy
  • Jesse Rothstein, Public Policy
  • Valerie Shapiro, Social Welfare
  • Jennifer Skeem, Social Welfare
  • Susan Stone, Social Welfare

Training faculty from Data Science

  • Laura Balzer, Biostatistics
  • David Bamman, EECS and School of Information
  • Joshua Blumenstock, School of Information
  • Patrick Bradshaw, Epidemiology
  • Sarah Chasins, EECS
  • Peng Ding, Statistics
  • Sandrine Dudoit, Statistics
  • Avi Feller, Public Policy and Statistics
  • Marta González, Planning
  • Alan Hubbard, Biostatistics
  • Michael Mahoney, Statistics
  • Aditya Parameswaran, EECS and School of Information
  • Fernando Pérez, Statistics
  • Maya Petersen, Biostatistics
  • Sam Pimentel, Statistics
  • Corinne Riddell, Biostatistics
  • Phillip Stark, Statistics
  • Mark van der Laan, Biostatistics
  • Jingshen Wang, Biostatistics

Participating Organizations

CSS Fellows

Topics

Training topic page, Fellowship Programs topic page
Computational Social Science Training Program image