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);  Patrick Bradshaw, professor of epidemiology of the UC Berkeley School of Public Health and Berkeley director of the UC Berkeley-UCSF Joint Program in Computational Precision HealthHeather A. Haveman, professor of Sociology and Business and BIDS Associate Director; and Tim Thomas, BIDS Research Training Lead for the Computational Social Science Training Program, and Research Director of Berkeley’s Urban Displacement Project.

For full details about this program’s implementation and training faculty, see the "Overview" below. Please contact with any questions.

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


The Berkeley Computational Social Science Training Program (CSSTP) is a new two-year multi-disciplinary training program in advanced data analytics for predoctoral students in the social and behavioral sciences. CSSTP aims to prepare social science researchers to tackle the complex health problems prioritized by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), including maternal and child health, adolescent health, pubertal timing, mental health, health disparities, and the social determinants of health.

CSSTP combines Berkeley’s long-standing strength in quantitative social and behavioral science with its nationally-recognized campus programs in data science education, practice, and research. It serves five trainees per year over five years. The training faculty includes 21 social scientists who have exemplary records of developing and applying novel statistical methods to health-related social/behavioral science problems, as well as 13 data scientists who are leading figures in the foundations of mathematics, statistics/biostatistics, and computer science.


Second- and third-year predoctoral students who meet the following requirements are eligible for the two-year CSSTP:

  • Enrolled in your PhD degree programs: Sociology, Demography, Epidemiology, Public Health Policy, Social Welfare, and Public Policy; and
  • Have completed the first-year course requirements in their home departments; and
  • Are US citizens or permanent residents.


During their first year in the program, trainees receive a 12-month, $25,000 stipend (with half dispersed at the start of each semester), travel funds, and 60% of their tuition and fees for fall and spring semesters. Their home departments are responsible for the remaining 40% of tuition and fees and for health insurance during the first year. Please note that:

  • NIH rules for the fellowship prohibit trainees from working more than 25% time during the 12-month period of their first-year trainee appointment; however, additional fellowship or stipend support is allowed as long as it does not come with work obligations
  • CSSTP does not provide direct funding for stipend nor tuition support for the second year of the program

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 CSSTP 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;
  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

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. 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 Ahearn, Epidemiology
  • Henry Brady, Public Policy
  • Julian Chow, Social Welfare
  • William Dow, Health Policy
  • Dennis Feehan, Demography
  • Josh Goldstein, Demography
  • David Harding, Sociology
  • Heather A. Haveman, Sociology
  • Hilary Hoynes, Public Policy
  • Sol Hsiang, Public Policy
  • Alan Hubbard, Epidemiology
  • Rucker Johnson, Public Policy
  • Jenna Johnson-Hanks, Sociology
  • Amy Lerman, Public Policy
  • Ayesha Mahmud, Demography
  • Mahasin Mujahid, Epidemiology
  • Ziad Obermeyer, Health Policy
  • Emily Ozer, Epidemiology & Health Policy
  • Jessie Rothstein, Public Policy
  • Daniel Schneider, Sociology
  • Jennifer Skeem, Social Welfare
  • Susan Stone, Social Welfare
  • Jingshen Wang, Public Health

Training faculty from Data Science

  • Joshua Blumenstock, School of Information
  • Patrick Bradshaw, Epidemiology
  • Peng Ding, Statistics
  • Anca Dragan, Electrical Engineering & Computer Sciences
  • Sandrine Dudoit, Statistics
  • Laurent El Ghaoui, Electrical Engineering & Computer Sciences
  • Avi Feller, Statistics, Public Policy
  • Michael Jordan, Electrical Engineering & Computer Sciences
  • Michael Mahoney, Statistics
  • Fernando Perez, Statistics
  • Maya Petersen, Biostatistics
  • Sam Pimentel, Statistics
  • Phillip Stark, Statistics
  • Mark van der Laan, Biostatistics

Participating Organizations

CSS Fellows

Nomination Requirements

Faculty mentors: The next Call for Applications is anticipated in Spring 2023. Sign up for the BIDS Mailing List to receive updates.

1) A one-page nomination letter that addresses the following:

  • The student’s ability in quantitative social science as demonstrated through coursework, research experience, and/or teaching
  • The student’s successful completion of first-year requirements in your department’s PhD program; and
  • Your availability to mentor the student as their social sciences faculty mentor.

2) A one-page statement from the student addressing the following:

  • Their enthusiasm for participation in a multidisciplinary intellectual environment and computational social science research related to health or the social determinants of health, broadly defined.
  • The alignment between their career goals and the program’s goals, and indicating their strong interest in a career in health research after graduation.

3) A copy of the student's undergraduate and graduate transcripts (unofficial copies are accepted).

4) The student’s current email address.

Questions about CSSTP? Send a message to

Computational Social Science Training Program image