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
University of Chicago
University of California
Ethics & Algorithms
U Washington IFDS faculty Dmitriy Drusvyatskiy receives the SIAM Activity Group on Optimization Best Paper Prize
This prize is awarded every three years to the author(s) of the most outstanding paper.
IFDS director Maryam Fazel receives Moorthy Professorship
IFDS director, UW ECE Professor, and Lytle Lectureship Chair Maryam Fazel (second from right) was selected as the first recipient of the Moorthy Family Inspiration Career Development Professorship.
IFDS workshop brings together data science experts to explore ways of making algorithms that learn from data more robust and resilient
The workshop focused on exploring “distributional robustness.” This is a promising framework and research area in data science aimed at addressing complex shifts and changes in data, which are fielded by automated devices and processes such as the algorithms used in AI and machine learning.
IFDS Affiliates Publish New Works on Robustness and Optimization
Two recent publications from Cambridge University Press are authored by IFDS affiliates.