The mission of the Brown TRIPODS institute is to foster development and principled application of theory and methods of big data to discover, refine, and validate underlying theoretical models that govern a system or data-generating process, which in turn improve predictions of new outcomes.
The Columbia TRIPODS Institute, hosted in the Data Science Institute at Columbia University, fosters research, education and center building around foundational topics that support the practice of data science, including (nonconvex) optimization, primitives for efficient computation, and interactive machine learning.
Cornell UniversityData Science for Improved Decision-Making: Learning in the Context of Uncertainty, Causality, Privacy, and Network Structures
This project creates a center of data science for improved decision-making that combines expertise from computer science, information science, mathematics, operations research, and statistics. The five concrete research directions proposed are: Privacy and Fairness, Learning on Social Graphs, Learning to Intervene, Uncertainty Quantification, and Deep Learning.
Georgia Institute of Technology
The Transdisciplinary Research Institute for Advancing Data Science (TRIAD) integrates research and education in mathematical, statistical, and algorithmic foundations for data science.
Massachusetts Institute of Technology
The MIT Institute for Foundations of Data Science (MIFODS) is an interdisciplinary effort to develop the theoretical foundations of data science through integrated research and training activities. Our goal is to stimulate research and educational interactions between mathematics, statistics and theoretical computer science, both within MIT and in the research community at large.
Northwestern University, Lehigh University, State University of New York at Stonybrook
The NSF TRIPODS Institute on Optimization and Learning, based at Lehigh University and in collaboration with Stony Brook and Northwestern Universities, has its current focus on new advances in tools for non-convex machine learning applications, in particular for various cases of training deep learning models.
Ohio State University
This center advances the methodological and theoretical foundations of data analytics by considering the geometric and topological aspects of complex data from mathematical, statistical and algorithmic perspectives, thus enhancing the synergy between the Computer Science, Mathematics, and Statistics communities.
University of Arizona
UA-TRIPODS is an integrated research and educational institute in data sciences at UA, with focus on theoretical foundations. The mission of UA-TRIPODS is to produce long-term and deep-level collaborative research among computer science, statistics, and mathematics, to build effective partnerships with domain sciences, local industry and business, and to outreach to the public community.
University of California–Berkeley
The UC Berkeley FODA (Foundations of Data Analysis) Institute will focus on deepening the theoretical foundations of data science, from basic education to cutting-edge research, and translating those foundational developments to data science practice in the diverse range of domains that generate data.
University of California–Santa Cruz
The UC–Santa Cruz TRIPODS effort brings together researchers from mathematics, statistics, and computer science to develop a unified theory of data science applied to uncertain and heterogeneous graph and network data. We collaborate closely with the D3 Data Science Research Center and Data Science Santa Cruz.
University of Washington
The UW Tripods Institute on Algorithmic Foundations of Data Science (ADSI) focuses on theoretical foundations and algorithms for data science. At their core, each of the disciplines of computer science, mathematics, and statistics has rich theories of complexity and robustness, which have influenced the design of the available tools used in real world computational problems. ADSI seeks new algorithms and design principles that unify ideas and provide a common language for addressing contemporary data science challenges.
University of Wisconsin–Madison
The Institute for Foundations of Data Science at UW-Madison is doing research in the fundamentals of data science by working collaboratively across traditional domain boundaries of mathematics, statistics, and computer science.