Research Workshop

Topological Data Analysis

Topological Data Analysis

April 26-30, 2021


  • Brittany Fasy (Mathematics, Montana)
  • Kathryn Hess (Mathematics, EPFL)
  • Sayan Mukherjee (Statistics, Duke)
  • Jose Perea (Mathematics, Michigan State)


In this age of rapidly increasing access to ever larger data sets, it has become clear that studying the “shape” of data using the tools of combinatorial and algebraic topology can lead to much deeper insights than other standard methods when analyzing complex data sets. Topological data analysis (TDA) is the exciting and highly active new field of research that encompasses these productive developments at the interface of algebraic topology, statistics, and data science. This workshop will consist of a small number of plenary one-hour lectures by leading researchers in the field, a larger number of contributed short talks from early-career researchers, live demos of software, a problem session, and a poster session.  The speakers will cover a wide range of topics, from theory to concrete applications of TDA in science and engineering. The goals of the workshop are to foster scientific interactions across the growing breadth of the applied topology community and to provide an opportunity for algebraic topologists, statisticians, and data scientists curious about this dynamic new field to learn more about it.

Confirmed Speakers

  • Lorin Crawford (Microsoft Research New England)
  • Sara Kalisnik (Bentley University)
  • Facundo Memoli (Ohio State)
  • Ezra Miller (Duke University)
  • Anthea Monod (Imperial College London)
  • Elizabeth Munch (Michigan State University)
  • Vidit Nanda (University of Oxford)
  • Katharine Turner (Australian National University)
  • Yusu Wang (UC San Diego)

Registration will open soon.