Matthew Hirn
Associate Professor
Michigan State University

Department of Computational Mathematics, Science & Engineering
Department of Mathematics
Center for Quantum Computing, Science & Engineering

College of Natural Science
College of Engineering

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I am a mathematician working at the interface of harmonic analysis, data science and machine learning, and I am the scientific leader of the CEDAR Team at Michigan State University. Please navigate below for information on various projects, or use the menu above to find information on papers, talks, code, teaching, and other things. If you are a graduate student or postdoc interested in working with me, please contact me utilizing the information in the Contact page.

Announcements

[July 16, 2020] My paper with Anna Little entitled Wavelet invariants for statistically robust multi-reference alignment has been accepted to Information and Inference. This is Anna’s and my first paper on the multi-reference alignment problem, which we were introduced to by a very nice talk at MSU by Amit Singer a few years back. After seeing his talk we were inspired to look into the problem in more detail, and decided to consider a modified and more difficult version of the problem. In the usual MRA problem one tries to recover a signal from random translates of the signal that have also been corrupted by additive white noise. In our modified version of the problem, we keep the random translations and additive white noise, but also corrupt the signals by random dilations, which can be used to model random stretching of molecules or random zoom in images. We worked really hard on this paper and are quite pleased with the results, so please check it out!

[June 28, 2020] I am very pleased to announce that my paper with Michael Perlmutter, Feng Gao, and Guy Wolf entitled Geometric Scattering Networks on Compact Riemannian Manifolds has been accepted to the Mathematical and Scientific Machine Learning (MSML) conference. This is the first edition of the MSML conference, and we are very excited to be a part of it. In this paper we extend the wavelet scattering transform to compact Riemannian manifolds and use this extension as a model for deep networks on such manifolds. We prove that these networks are provably invariant to isometry group actions, and stable to diffeomorphism actions on input signals supported on the manifold. This paper is part of a series of works on geometric wavelet scattering networks on graphs and manifolds, but is our first comprehensive manifold-based paper (there was a preliminary version of this work in NeurIPS workshop in 2018).

[June 14, 2020] Guy Wolf and I are organizing an online SIAM MDS mini-symposium on “Deep thoughts on geometric learning & exploration of non-Euclidean data.” It will be held June 17 and June 24 and we have a great list of speakers. Anyone can join via Zoom – more information here. UPDATE [June 27, 2020]: The mini-symposium has concluded! Slides from the talks can be found on the mini-symposium page.

[April 27, 2020] There is an exciting online seminar series on the Mathematics of INformation, Data, and Signals (MINDS), with a really great list of speakers. Anyone can join virtually through Zoom, you just need to sign up through the Google form here.