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 transdisciplinary 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.
The IFDS is supported by a $1.5 million grant from the National Science Foundation’s Transdisciplinary Research in Principles of Data Science (TRIPODS) initiative.
