Risi Kondor

Risi Kondor homepage

Risi Kondor
Associate Professor
Department of Computer Science (Crerar 325)
Department of Statistics, and the
Computational and Applied Mathematics Initiative (CAMI) (Jones 122A)
The University of Chicago

email address





NEW:    R. Kondor, Z. Lin and S. Trivedi:  Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network (preprint 6/24/18)

NEW:    T. Son Hy, S. Trivedi, H. Pan, B. M. Anderson and R. Kondor:  Predicting Molecular Properties with Covariant Compositional Networks (JCP special issue on data enabled theoretical chemistry, June 2018) [pdf]

NEW:    R. Kondor:  N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials (preprint 3/5/18)

NEW:    R. Kondor and S. Trivedi:  On the generalization of equivariance and convolution in neural networks to the action of compact groups (ICML 2018) [poster] [slides]

Current    Research    Multiresolution/multiscale matrix factorizations
Machine learning for many-particle physics
Learning (on) graphs and other combinatorial structures
Permutation problems and Fourier analysis on the symmetric group
NEW:  Generalized covariant neural network architectures

Postdoc    Brandon Anderson

Students    Yi Ding (CS PhD, 3rd year)
Jonathan Eskreis-Winkler (Stats PhD, 4th year)
Hy Truong Son (CS PhD, 2nd year)
Pramod K Mudrakarta (CS PhD, 4th year)
Horace Pan (CS PhD, 3rd year)

Other student    collaborators    Vikas Garg (MIT)
Shubhendu Trivedi (TTI-C)

Graduated   
students   

Nedelina Teneva (Amazon)
Maia Fraser (assistant prof. at the University of Ottawa)

Recent papers    R. Kondor, Z. Lin and S. Trivedi:  Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network (preprint 6/24/18)

T. Son Hy, S. Trivedi, H. Pan, B. M. Anderson and R. Kondor:  Predicting Molecular Properties with Covariant Compositional Networks (JCP special issue on data enabled theoretical chemistry, June 2018) [pdf]

R. Kondor:  N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials (preprint 3/5/18)

R. Kondor and S. Trivedi:  On the generalization of equivariance and convolution in neural networks to the action of compact groups (ICML 2018)

R. Kondor, Hy Truong Son, Horace Pan, Brandon Anderson, Shubhendu Trivedi:  Covariant compositional networks for learning graphs (preprint) [PyTorch code] [GraphFlow] [video]

Y. Ding, R. Kondor and J. Eskreis-Winkler:  Multiresolution kernel approximation for Gaussian process regression (NIPS 2017)

P. K. Mudrakarta and R. Kondor:  A generic multiresolution preconditioner for sparse symmetric systems (preprint)

K. Rajendran, A. A. Kattis, A. Holiday, R. Kondor and I. G. Kevrekidis:  Data mining when each data point is a network (to appear in "Patterns of Dynamics", Springer Proceedings)

V. Ithapu, R. Kondor and V. Singh:  The Incremental Multiresolution Matrix Factorization Algorithm (CVPR 2017)

R. Kondor and H. Pan:  The Multiscale Laplacian Graph Kernel (NIPS 2016 oral presentation)  [arXiv] [code] [slides]

N. Teneva, P. K. Mudrakarta and R. Kondor:  Multiresolution Matrix Compression (AISTATS 2016, winner of notable student paper award)

R. Kondor, N. Teneva and P. K. Mudrakarta:  Parallel MMF:  a Multiresolution Approach to Matrix Computation (arXiv preprint, July 2015) [pMMF library]

G. Plumb, D. Pachauri, R. Kondor and V. Singh:  SnFFT:  a Julia Toolkit for Harmonic Analysis on the Symmetric Group (JMLR 2015)

D. Pachauri, R. Kondor, G. Sargur and V. Singh:  Permutation diffusion maps with application to the image association problem in computer vision (NIPS 2014)

R. Kondor, N. Teneva and V. Garg:  Multiresolution Matrix Factorization (ICML 2014) [supplement] [video]

Recent events    Multiresolution Methods for Large Scale Learning workshop at NIPS 2015 [videos]

IMA Summer School on Modern Applications of Representation Theory Chicago, July 21 - August 6, 2014  (co-organized with Jason Morton and Lek-Heng Lim) [videos]

Software    SnOB: a C++ library for computing fast Fourier transforms on the symmetric group

pMMF: a high performance C++ library for parallel Multiresolution Matrix Factorization

Mondrian:  C++ parallel blocked matrix library

GraphFlow: a C++ deep learning library with support for covariant compositional architectures (Hy Truong Son)

Links    All papers by topic
Curriculum Vitae
Detached threads blog