Berkeley and MIT receive NSF TRIPODS award to lead the “Foundations of Data Science Institute”

September 1, 2020

A team led by Berkeley and MIT researchers has recently received a National Science Foundation Transdisciplinary Research in Principles of Data Science (NSF TRIPODS) award to form the new Foundations of Data Science Institute (FODSI), a TRIPODS Phase II institute growing directly from the Foundations of Data Analysis Institute (FODA), a 2017 TRIPODS Phase I award that is currently led by BIDS Faculty Affiliates Michael Mahoney, Bin Yu, and Fernando Perez, with Michael Jordan and Peter Bartlett.  

By leveraging the success of FODA, FODSI will take this important research to the next level in forming a collaborative research endeavor to delve deeper into foundational issues in data science — including modeling, inference, computational efficiency and societal issues — with the potential for major impacts across disciplinary boundaries.

The new team will also include researchers from Boston, Northeastern, Harvard and Howard Universities; and Bryn Mawr College; with diverse academic backgrounds in computer science, statistics, mathematics, electrical engineering, and economics. BIDS Faculty Affiliate and Statistics/EECS professor Bin Yu will be a co-PI, and the team will be led by FODSI director Peter Bartlett (professor of EECS and Associate Director of the Simons Institute for the Theory of Computing at UC Berkeley) and co-director Piotr Indyk, a professor of EECS and a member of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). 

Over the course of the last century, data science has enabled unprecedented progress in a multitude of research areas, and now plays a pivotal role in diverse domains across science, industry and commerce.  The theoretical foundations of data science are innately connected to a variety of important issues, including how effectively models and algorithms are designed, how specifically they incorporate the goals of the analysis, and how efficiently they manage the interactions, interventions, and feedback mechanisms that affect the data and the interpretation of results.  

By considering solutions from a variety of different disciplines and perspectives, FODSI research will encompass the full range of foundational issues that arise during the course of the data science life cycle, address the challenges that emerge when conflicts arise between competing requirements and decision-makers, and shed light on the economic, social, and ethical implications of automated data analysis and decision-making. And by cultivating connections with industry partners -- including researchers at Amazon, Google, LinkedIn, Microsoft, and Verizon Media -- FODSI will maintain a consistent focus on real-world applications in data science.

The new institute will also present a robust educational component, including collaborative research workshops, the further development of an undergraduate data science program and an annual summer school for advanced undergraduates, advanced training and mentorship activities for graduate students and postdocs, and a variety of public outreach activities targeting diverse audiences and under-represented groups.

Read more...

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Featured Fellows

Michael Mahoney

Statistics, UC Berkeley
Faculty Affiliate

Bin Yu

Statistics, UC Berkeley
Faculty Affiliate

Fernando Pérez

Statistics, UC Berkeley; Data Science and Technology, LBNL
Faculty Affiliate

Peter L. Bartlett

Computer Science, Statistics, UC Berkeley
Alumni - Senior Fellow