NEW: 
R. Kondor, Z. Lin and S. Trivedi:
ClebschGordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
(NeurIPS 2018)

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:
Nbody 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 manyparticle 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, 4th year)
Jonathan EskreisWinkler (Stats PhD, 5th year)
Hy Truong Son (CS PhD, 3rd year)
Pramod K Mudrakarta (CS PhD, 5th year)
Horace Pan (CS PhD, 4th year)

Other student collaborators 
Vikas Garg (MIT)
Shubhendu Trivedi (TTIC)

Graduated students

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

Courses 
Winter 2019: CMSC 25400/STAT 27725 Machine Learning (undergraduate)
Spring 2019: STAT 37710/CMSC 35400 Machine Learning (graduate)
Spring 2019: CMSC 354011 Topics:
High performance ML system design

Recent papers 
R. Kondor, Z. Lin and S. Trivedi:
ClebschGordan 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:
Nbody 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. EskreisWinkler:
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
(coorganized with Jason Morton and LekHeng 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
