Mission

As data continues to accumulate at an ever increasing rate, so does the need for powerful and novel methods to extract information from data, in a form that is useful to individuals, society, researchers, and commerce. Research into the fundamentals of data science brings people with expertise in mathematics, statistics, and theoretical computer science together in a concerted transdiciplinary effort to explore new approaches to the formulation and solution of problems in data analysis. A previous generation of researchers at UW-Madison made foundational contributions to data science, in such areas as kernel learning, splines, and experimental design. The current generation of faculty is carrying forward this tradition. During the past decade, researchers across campus have done important work in diverse aspects of fundamental data science, as well as in applications to numerous areas of domain science, engineering, and medicine. This group forms the core of the Institute for Foundations of Data Science (IFDS) at UW-Madison. Building on previous work, and pursuing new goals sparked at the interfaces of mathematics, statistics, and theoretical computer science, IFDS aims to produce excellent research and to epitomize the possibilities of collaborative approach to investigating fundamental issues in data science.

News

IFDS Supporting Four Research Assistants in Spring 2018
IFDS Supporting Four Research Assistants in Spring 2018
The funding uses NSF grant monies under the TRIPODS and Convergence Programs.
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Nowak Becomes Section Editor of New SIAM Journal on the Mathematics of Data Science
Nowak Becomes Section Editor of New SIAM Journal on the Mathematics of Data Science
The new journal will begin to take author submissions in Spring 2018.
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Willett and Raskutti Build Better Tools for Big Data
Willett and Raskutti Build Better Tools for Big Data
In the era of Big Data, researchers like electrical and computer engineer Rebecca Willett and statistics professor Garvesh Raskutti, both part of the Institute for Foundations of Data Science at...
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IFDS Members Help Lead Semester Program on Statistical Scalability at the University of Cambridge
IFDS Members Help Lead Semester Program on Statistical Scalability at the University of Cambridge
The program includes four week-long workshops, on topics ranging from the algorithmic underpinnings of Big Data analysis to the heterogeneity and geometry of large-scale datasets.
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IFDS members in Oaxaca Workshop “Beyond Convexity”
IFDS members in Oaxaca Workshop “Beyond Convexity”
The gathering concentrated on a vital emerging issue in data science: formulation of data science problems as nonconvex optimization problems, and algorithms for solving them.
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Advances in Nonconvex Optimization Impact Data Analysis
Advances in Nonconvex Optimization Impact Data Analysis
Algorithmic tools from optimization have had become more and more important in machine learning and data analysis over the past 15 years. For much of...
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