Papers

Geometric Deep Learning

• MagNet: A Magnetic Neural Network for Directed Graphs.
  With Xitong Zhang, Yixuan He, Nathan Brugnone, and Michael Perlmutter.
  In Advances in Neural Information Processing Systems 34, 2021.
  pdf, arXiv, NeurIPS. Software.
• ClassicalGSG: Prediction of logP Using Classical Molecular Force Fields and Geometric Scattering for Graphs.
  With Nazanin Donyapour and Alex Dickson.
  Journal of Computational Chemistry, volume 42, issues 14, pages 1006-1017, 2021.
  pdf, ChemRxiv, Journal of Comp. Chem. Software.
• Geometric scattering networks on compact Riemannian manifolds.
  With Michael Perlmutter, Feng Gao and Guy Wolf.
  Proceedings of The First Mathematical and Scientific Machine Learning Conference, Proceedings of Machine Learning Research, volume 107, pages 570–604, 2020.
  pdf, arXiv, PMLR.
• Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms.
  With Michael Perlmutter, Feng Gao and Guy Wolf.
  2019.
  pdf, arXiv.
• Geometric wavelet scattering on graphs and manifolds.
  With Feng Gao, Michael Perlmutter and Guy Wolf.
  In Proceedings of SPIE 11138, Wavelets and Sparsity XVIII, 111380Q, 2019.
  pdf, SPIE.
• Geometric Scattering for Graph Data Analysis.
  With Feng Gao and Guy Wolf.
  In Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research (PMLR), volume 97, pages 2122-2131, 2019.
  pdf, ICML/PMLR, arXiv.
• Geometric Scattering on Manifolds (contributed spotlight talk).
  With Michael Perlmutter and Guy Wolf.
  In NeurIPS Workshop on Integration of Deep Learning Theories, Montreal, Canada, December 8, 2018.
  pdf, arXiv, NIPS DLTheory.

Geometric Data Analysis and Manifold Learning

• Coarse Graining of Data via Inhomogeneous Diffusion Condensation.
  With Nathan Brugnone, Alex Gonopolskiy, Mark W. Moyle, Manik Kuchroo, David van Dijk, Kevin R. Moon, Daniel Colon-Ramos, Guy Wolf and Smita Krishnaswamy.
  In Proceedings of the 2019 IEEE International Conference on Big Data, pages 2624–2633, 2019.
  pdf, arXiv, IEEE Xplore. Software.
• Visualizing Structure and Transitions for Biological Data Exploration (PHATE).
  With Kevin R. Moon, David van Dijk, Zheng Wang, Scott Gigante, Daniel Burkhardt, William Chen, Kristina Yim, Antonia van den Elzen, Ronald R. Coifman, Natalia B. Ivanova, Guy Wolf and Smita Krishnaswamy.
  Nature Biotechnology, volume 37, pages 1482–1492, 2019.
  pdf (journal version), pdf (complete version, very large > 100 mb), bioRxiv, Nature Biotechnology. Software.
• Time Coupled Diffusion Maps.
  With Nicholas Marshall.
  Applied and Computational Harmonic Analysis, volume 45, issue 3, pages 709-728, 2018.
  pdf, arXiv, ScienceDirect.
• A Diffusion-based Condensation Process for Multiscale Analysis of Single Cell Data.
  With Tobias Welp, Guy Wolf and Smita Krishnaswamy.
  In ICML Workshop Computational Biology, New York, June 24, 2016. 5 pages.
  pdf, ICML WCB.
• Diffusion maps for changing data.
  With Ronald R. Coifman.
  Applied and Computational Harmonic Analysis, volume 36, issue 1, pages 79-107, January 2014.
  pdf, arXiv, ScienceDirect. Software.
• Bi-stochastic kernels via asymmetric affinity functions.
  With Ronald R. Coifman.
  Applied and Computational Harmonic Analysis, volume 35, issue 1, pages 177-180, July 2013.
  pdf, arXiv, ScienceDirect.
• Frame based kernel methods for automatic classification in hyperspectral data.
  With John J. Benedetto, Wojciech Czaja and Justin C. Flake.
  In Proceedings of the IEEE 2009 International Geoscience and Remote Sensing Symposium, volume 4, pages 697-700, Cape Town, South Africa, July 12-17, 2009.
  pdf, IEEE Xplore.