Risi Kondor

Risi Kondor homepage

Risi Kondor
Associate Professor
Computer Science, Statistics &
The Committee on Computational and Applied Mathematics (CCAM)

The Machine Learning Group
The University of Chicago, Ryerson 257B
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