The CEDAR (ComplEx Data Analysis Research) Team works at the interface of harmonic analysis and machine learning. We develop tools that uncover complex, multiscale patterns in high dimensional data by considering the underlying data geometries, invariants, hierarchies and statistics. Our focus is on rigorous mathematical theory coupled with state of the art numerical results in application specific domains. Research areas include:

  • Wavelet theory and deep learning (scattering transforms)
  • Machine learning and many body problems in quantum chemistry and materials science
  • Geometric data analysis and manifold learning
  • Smooth (Whitney) extensions and interpolations of data
  • High dimensional data analysis, including bio-medical data
The CEDAR Team (Spring 2018). From left to right: Matthew Hirn, Nathan Brugnone, Michael Perlmutter, Jieqian He, Ryan LaRose, Feng Gao, Xavier Brumwell, Anna Little, Paul Sinz

Team members

Scientific leader

Matthew Hirn [CMSE, Mathematics]


Graduate students

  • Nathan Brugnone [Community Sustainability, CMSE] (PhD advisors: Robert Richardson, Matthew Hirn)
  • Xavier Brumwell [CMSE] (PhD advisor: Matthew Hirn)
  • Feng Gao [Plant, Soil and Microbial Sciences, CMSE] (PhD advisor: Stephen Boyd)
  • Jieqian He [CMSE, Statistics] (PhD advisor: Matthew Hirn)
  • Ryan LaRose [CMSE, Physics] (PhD advisor: Matthew Hirn)


Previous pictures

The CEDAR Team (Fall 2017). From left to right: Jieqian He, Feng Gao, Ryan LaRose, Xavier Brumwell, Michael Perlmutter, Paul Sinz, Nathan Brugnone, Matthew Hirn