Data Science Planning Initiative

Berkeley is investing in data science. The university is strengthening foundational areas of the field, advancing its reach in domains where it is put to work, and shaping it with Berkeley’s commitment to critical reflection and the public good.

Berkeley’s prospects in data science are marked by new educational programs, campus-wide organizational activity, signature projects, and large-scale investments of federal, industry, and philanthropic support. In the long view, Berkeley’s planning initiative builds on strategic commitments already made in faculty strength and research areas across the university. In the near term, it draws energy and excitement from an upsurge of data science-related activity in research and teaching.

The university’s planning initiative for data science is led by faculty and tied into the university’s priorities for strategic planning and fundraising. Data science is an integrative force for Berkeley across this rich, connected terrain.

Vision for Data Science at Berkeley

Berkeley recognizes that the transformative potential of data science—its pervasiveness, significance, and continuing evolution—opens new frontiers of university research and teaching. With intellectual roots in computer science, mathematics, statistics, as well as a variety of other disciplines, data science now touches many domains, from the natural and social sciences to the humanities and the professions.

Our vision for data science at Berkeley encompasses all of its components: pushing the conceptual frontiers of the field, applying established or emerging technology to new areas or domains, and studying the normative implications of the explosion of data and analysis for policy and society. Accordingly, the data science planning initiative is chartered to address the full spectrum of data science activity at Berkeley. This includes areas as diverse as computational social science, global climate studies, public health, precision medicine, and urbanization; work on the ethical dilemmas and social implications of data analytics; and advances in foundations such as statistical machine learning and cloud computing platforms.

Goals and Work

The data science planning initiative is led by faculty and aligned with regular Academic Senate processes and campus-level strategic planning. It aims to advance Berkeley’s excellence in fields around data and computing and to connect them fluidly across the campus. The initiative creates pathways for the university to move flexibly and decisively into new domains. It is charged to recommend opportunities and strategies for research, teaching, organization, and fundraising. The reach of Berkeley’s new data science education program serves as a model.

The initiative builds upon our outstanding research and graduate programs and our pathbreaking undergraduate curriculum. It draws together our wide-ranging excellence by engaging our community in its formative stages, and it integrates our intention to advance the university’s public mission by engaging with the societal, humanistic, and policy ramifications of data science. The initiative is defining pathways of institutional development that can fit the world’s leading public university for the 21st-century world.

Faculty Advisory Board

The data science Faculty Advisory Board is charged with developing a statement of vision and strategy for Berkeley’s global leadership in data science. Its work involves assessing opportunities to strengthen data science across the university and synthesizing perspectives into an actionable plan. At the end of Spring 2016, the FAB will provide a white paper to the Chancellor and Provost, along with supporting materials for the administration and the campus to be made available on this site. 

The FAB has been constituted to touch many areas of campus and draw on broad experience in administration and campus governance. It is an advisory rather than a representative or a governing body.

Charge of the Faculty Advisory Board 

The Faculty Advisory Board (FAB) of the Data Science Planning Initiative is charged with developing an integrated strategy for Berkeley’s global leadership in data science. In its advisory role to the Chancellor and Provost, it will chart paths of institutional development that can support a comprehensive initiative in data science, drawing out and advancing Berkeley’s distinctive strengths throughout this domain. It is expected that the DSPI will encourage the confluence of data science activities across the full range of our research and teaching, including the core specialty areas of data science, its use in research domains across campus, and its broader societal and normative entanglements. In developing its vision, the FAB will assess our university-wide strengths, gaps, connections, and opportunities in data science and address questions of coordination, faculty hiring, resourcing, fundraising strategy, and organizational forms. In order to prepare for significant investment in this area, the DSPI will engage the campus community in the formative stages of its process. In Fall 2016, the FAB documented its strategic vision in the form of a white paper for the Chancellor and Provost, together with supporting materials.


David E. Culler, Co-Director
Friesen Professor of Computer Science
Department of Electrical Engineering & Computer Sciences

AnnaLee (Anno) Saxenian, Co-Director
Dean and Professor
School of Information

Bob Jacobsen, Cognizant Dean
College of Letters & Science

Cathryn Carson, Chair of the Faculty Advisory Board
Department of History

Faculty Advisory Board

Lisa García Bedolla

Graduate School of Education and Political Science

Francesco Borrelli

Mechanical Engineering

Cathryn Carson


Ron Cohen

Chemistry and Earth & Planetary Sciences

David Culler

Electrical Engineering & Computer Sciences

Rosemary Gillespie

Environmental Science, Policy & Management

Sol Hsiang

Goldman School of Public Policy

Bob Jacobsen

Physics and L&S Undergraduate Division

Michael Jordan

Statistics and Electrical Engineering and Computer Sciences

Susan Marqusee

Molecular & Cell Biology

Anno Saxenian

School of Information

Jas Sekhon

Political Science and Statistics

Chris Shannon

Economics and Mathematics

Ion Stoica

Electrical Engineering & Computer Sciences

Bin Yu

Statistics and Electrical Engineering and Computer Sciences