Data science is making an enormous impact on science and society, but its success is uncovering pressing new challenges that stand in the way of further progress. Outcomes and decisions arising from many machine learning processes are not robust to errors and corruption in the data; data science algorithms are yielding biased and unfair outcomes, as concerns about data privacy continue to mount; and machine learning systems suited to dynamic, interactive environments are less well developed than corresponding tools for static problems. Only by an appeal to the foundations of data science can we understand and address challenges such as these.
Building on the work of three TRIPODS Phase I institutes, the new Institute for Foundations of Data Science (IFDS) brings together researchers from the Universities of Washington, Wisconsin-Madison, California-Santa Cruz, and Chicago, with the goal of tackling these critical issues. IFDS organizes its research around four core themes: complexity, robustness, closed-loop data science, and ethics and algorithms. By making concerted progress on these fundamental fronts, IFDS aims to lower several of the barriers to better understanding of data science methodology and to its improved effectiveness and wider relevance to application areas. In concert with its research agenda, IFDS engages the data science community through workshops, summer schools, and hackathons, and is committed to equity and inclusion through extensive plans for outreach to traditionally underrepresented groups.
University of Washington
University of Wisconsin
Madison
University of Chicago
University of California
Santa Cruz
Complexity
Robustness
Closed-loop
Data Science
Ethics & Algorithms
The Latest
Announcing the IFDS Workshop on “Theoretical Foundations of Applied AI” at the University of Washington, August 13-15, 2025
This workshop brings together a diverse group of theoreticians and applied researchers to explore the theoretical underpinnings behind the successes and limitations of modern AI.
Ilias Diakonikolas wins ACM Grace Hopper Award for breakthrough techniques in algorithm design
Ilias Diakonikolas, Sheldon B. Lubar professor of Computer Sciences, received the 2024 Association for Computing Machinery (ACM) Grace Murray Hopper Award, one of the highest honors for young researchers in computing.
Federal budget cuts threaten to decimate America’s AI superiority—and other countries are watching
In an op-ed for Fortune magazine, Rebecca Willett, IFDS executive committee member, and Henry Hoffmann discuss how federal research cuts threaten to devastate America’s innovation and dominance in artificial intelligence.
Data Meets Dynamics: Workshop on Data Assimilation for Complex Systems and Applications coming to Wisconsin in August 2025
This event is a collaboration between the IFDS at UW-Madison and the Data Science Center at Brigham Young University (BYU), a key partner of the UW IFDS.