University of Pennsylvania

The Penn Institute for Foundations of Data Science (PIFODS) brings together scientists and ideas from multiple disciplines, including computer science, electrical engineering, statistics, and mathematics, in order to collectively develop long-lasting principles for data science that can serve the field for decades to come. Specific research focuses of the institute include developing principles for complex learning tasks; for efficient optimization (convex, non-convex, and submodular); for streaming, distributed, and massively parallel data analysis; for privacy-preserving and fairness-preserving data analysis; and for reproducible data analysis.

Johns Hopkins University

The Mathematical Institute for Data Science (MINDS) brings together a multidisciplinary team of mathematicians, statisticians, theoretical computer scientists, and electrical engineers from Johns Hopkins University to develop the foundations of deep neural models (e.g., feedforward networks) and graphical models data (e.g., random graphs), with the ultimate goal of arriving at integrated models that are more interpretable, robust to perturbations, and learnable with minimal supervision. In addition, the institute will foster interactions among data scientists through a monthly seminar series, semester-long research themes, an annual research symposium, and a summer research school and workshop on the foundations of data science.

Duke University

The Transdisciplinary Research and Education Collective at Duke University (TREC@Duke) was established to further our understanding of foundational principles in data science and to identify opportunities for innovation by working across disciplines. TREC@Duke focuses on creating new tools, methods, and dialogues in data science throughout North Carolina’s “Research Triangle.”

University of Massachusetts

The UMass TRIPODS Institute brings together faculty from computer science, mathematics and statistics, and electrical engineering, along with post-docs, undergraduate and graduate students, and high school students, for research and training in the foundations of data science. Research interests include algorithms for massive data sets, computational and statistical trade-offs, quantifying uncertainty, and interactive data acquisition. Application areas include biomedical and chemical engineering.

University of California, Davis

UCD4IDS is composed of 35 researchers (four PIs and 31 senior participants) coming from four departments (Computer Science, Electrical & Computer Engineering, Mathematics, and Statistics) and will cross interdepartmental barriers and promote interdisciplinary research collaborations among faculty members, postdocs, and graduate students, particularly focusing on: 1) Fundamentals of machine learning directed toward biological and medical applications; 2) Optimization theory and algorithms for machine learning; and 3) High-dimensional data analysis on graphs and networks.

Northwestern University

The Institute for Data, Econometrics, Algorithms, and Learning (IDEAL) is a multi-discipline (computer science, statistics, economics, electrical engineering, and operations research) and multi-institution (Northwestern University, Toyota Technological Institute at Chicago, and University of Chicago) collaborative institute that focuses on key aspects of the theoretical foundations of data science. The institute will support the study of foundational problems related to machine learning, high-dimensional data analysis and optimization in both strategic and non-strategic environments. The primary activity of the institute will be thematically focused quarters which will coordinate graduate course work with workshops and external visitors.

University of Texas at Austin

UT Austin’s TRIPODS IFDS beings together 8 faculty from 4 departments – ECE, CS, Statistics and Mathematics – to provide a highly collaborative and productive environment for research into three main thrust areas: (a) advancing the theoretical understanding of training and generalization in neural networks, (b) rigorous approaches to robustness in machine learning, and (c) incorporating and using graphical structure into how data is modeled and used. The institute is synergistic with a recently announced NSF AI Institute at UT Austin.

University of Illinois at Chicago

This collaborative research institute combines aspects of mathematics, statistics, computer science, and electrical engineering to study the foundations of data science. The research focus of the institute is centered around the topics of: representation and structure of data, machine learning and complexity, and robustness and privacy.

Iowa State University

The D4 (Dependable Data Driven Discovery) Institute at Iowa State University is focused on advancing the theoretical foundations of data science by fostering foundational research to enable understanding of the risks to the dependability of data-science lifecycles, to formalize the rigorous mathematical basis of the measures of dependability for data science lifecycles, and to identify mechanisms to create dependable data-science lifecycles.

Texas A&M University

The Texas A&M Research Institute for Foundations of Interdisciplinary Data Science (FIDS) will bring together researchers from six disciplinary areas, Statistics, Electrical Engineering, Mathematics, Computer Science, Industrial & Systems Engineering, and Operation Management to conduct research on the foundations of data science motivated by problems arising in bioinformatics, the energy arena, power systems, and transportation systems. This Institute will be well positioned to develop rigorous theories, novel methodologies, and efficient computational techniques to solve data challenges in many other application domains.