Development of an untethered, mobile, low-cost head-mounted eye tracker. - Elizabeth S. Kim; Adam Naples; Giuliana Vaccarino Gearty; Quan Wang 003; Seth Wallace; Carla A. Wall; Michael Perlmutter; Fred Volkmar; Frederick Shic; Linda Friedlaender; Jennifer Kowitt; Brian Reichow (ETRA, 2014)

Mapping connections between biological-emotional preferences and affective recognition: An eye-tracking interface for passive assessment of emotional competency. - Carla A. Wall; Quan Wang 003; Mary Weng; Elizabeth S. Kim; Litton Whitaker; Michael Perlmutter; Frederick Shic (ACII, 2015)

Linking volitional preferences for emotional information to social difficulties: A game approach using the microsoft kinect. - Mary Weng; Carla A. Wall; Elizabeth S. Kim; Litton Whitaker; Michael Perlmutter; Quan Wang 003; Eli R. Lebowitz; Frederick Shic (ACII, 2015)

Geometric Scattering on Manifolds. - Michael Perlmutter; Guy Wolf; Matthew J. Hirn (CoRR, 2018)

Inverting Spectrogram Measurements via Aliased Wigner Distribution Deconvolution and Angular Synchronization. - Michael Perlmutter; Sami Merhi; Aditya Viswanathan; Mark A. Iwen (CoRR, 2019)

Understanding Graph Neural Networks with Asymmetric Geometric Scattering Transforms. - Michael Perlmutter; Feng Gao; Guy Wolf; Matthew J. Hirn (CoRR, 2019)

A Provably Accurate Algorithm for Recovering Compactly Supported Smooth Functions from Spectrogram Measurements. - Michael Perlmutter; Nada Sissouno; Aditya Viswanathan; Mark A. Iwen (EUSIPCO, 2020)

Geometric Wavelet Scattering Networks on Compact Riemannian Manifolds. - Michael Perlmutter; Feng Gao; Guy Wolf; Matthew J. Hirn (MSML, 2020)

A Hybrid Scattering Transform for Signals with Isolated Singularities. - Michael Perlmutter; Jieqian He; Mark A. Iwen; Matthew J. Hirn (ACSCC, 2021)

MagNet: A Neural Network for Directed Graphs. - Xitong Zhang; Yixuan He; Nathan Brugnone; Michael Perlmutter; Matthew J. Hirn (NeurIPS, 2021)

MagNet: A Magnetic Neural Network for Directed Graphs. - Xitong Zhang; Nathan Brugnone; Michael Perlmutter; Matthew J. Hirn (CoRR, 2021)

Phase Retrieval for L2([-π, π]) via the Provably Accurate and Noise Robust Numerical Inversion of Spectrogram Measurements. - Mark A. Iwen; Michael Perlmutter; Nada Sissouno; Aditya Viswanathan (CoRR, 2021)

Modewise Operators, the Tensor Restricted Isometry Property, and Low-Rank Tensor Recovery. - Mark A. Iwen; Deanna Needell; Michael Perlmutter; Elizaveta Rebrova (CoRR, 2021)

Molecular Graph Generation via Geometric Scattering. - Dhananjay Bhaskar; Jackson D. Grady; Michael A. Perlmutter; Smita Krishnaswamy (CoRR, 2021)

Towards a Taxonomy of Graph Learning Datasets. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2021)

Toward Fast and Provably Accurate Near-field Ptychographic Phase Retrieval. - Mark A. Iwen; Michael Perlmutter; Mark Philip Roach (CoRR, 2021)

On Audio Enhancement via Online Non-Negative Matrix Factorization. - Andrew Sack; Wenzhao Jiang; Michael Perlmutter; Palina Salanevich; Deanna Needell (CISS, 2022)

Scattering Statistics of Generalized Spatial Poisson Point Processes. - Michael Perlmutter; Jieqian He; Matthew J. Hirn (ICASSP, 2022)

MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian. - Yixuan He; Michael Perlmutter; Gesine Reinert; Mihai Cucuringu (LoG, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Sarah McGuire; Xinyi Wang; Anna Little; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; Ladislav Rampásek (LoG, 2022)

Molecular Graph Generation via Geometric Scattering. - Dhananjay Bhaskar; Jackson D. Grady; Egbert Castro; Michael Perlmutter; Smita Krishnaswamy (MLSP, 2022)

Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem? - Yimeng Min; Frederik Wenkel; Michael Perlmutter; Guy Wolf (NeurIPS, 2022)

The Manifold Scattering Transform for High-Dimensional Point Cloud Data. - Joyce A. Chew; Holly R. Steach; Siddharth Viswanath; Hau-Tieng Wu; Matthew J. Hirn; Deanna Needell; Matthew D. Vesely; Smita Krishnaswamy; Michael Perlmutter (TAG-ML, 2022)

Overcoming Oversmoothness in Graph Convolutional Networks via Hybrid Scattering Networks. - Frederik Wenkel; Yimeng Min; Matthew J. Hirn; Michael Perlmutter; Guy Wolf (CoRR, 2022)

Can Hybrid Geometric Scattering Networks Help Solve the Maximal Clique Problem? - Yimeng Min; Frederik Wenkel; Michael Perlmutter; Guy Wolf (CoRR, 2022)

Taxonomy of Benchmarks in Graph Representation Learning. - Renming Liu; Semih Cantürk; Frederik Wenkel; Dylan Sandfelder; Devin Kreuzer; Anna Little; Sarah McGuire; Leslie O'Bray; Michael Perlmutter; Bastian Rieck; Matthew J. Hirn; Guy Wolf; et al. (CoRR, 2022)

The Manifold Scattering Transform for High-Dimensional Point Cloud Data. - Joyce A. Chew; Holly R. Steach; Siddharth Viswanath; Hau-Tieng Wu; Matthew J. Hirn; Deanna Needell; Smita Krishnaswamy; Michael Perlmutter (CoRR, 2022)

Geometric Scattering on Measure Spaces. - Joyce A. Chew; Matthew J. Hirn; Smita Krishnaswamy; Deanna Needell; Michael Perlmutter; Holly R. Steach; Siddharth Viswanath; Hau-Tieng Wu (CoRR, 2022)

A Convergence Rate for Manifold Neural Networks. - Joyce A. Chew; Deanna Needell; Michael Perlmutter (CoRR, 2022)

Wire Before You Walk. - Tesfa Asmara; Dhananjay Bhaskar; Ian Adelstein; Smita Krishnaswamy; Michael Perlmutter (ACSSC, 2023)

A Flow Artist for High-Dimensional Cellular Data. - Kincaid MacDonald; Dhananjay Bhaskar; Guy Thampakkul; Nhi Nguyen; Joia Zhang; Michael Perlmutter; Ian Adelstein; Smita Krishnaswamy (MLSP, 2023)

Manifold Filter-Combine Networks. - Joyce A. Chew; Edward De Brouwer; Smita Krishnaswamy; Deanna Needell; Michael Perlmutter (CoRR, 2023)

Graph topological property recovery with heat and wave dynamics-based features on graphs. - Dhananjay Bhaskar; Yanlei Zhang; Charles Xu 004; Xingzhi Sun 003; Oluwadamilola Fasina; Guy Wolf; Maximilian Nickel; Michael Perlmutter; Smita Krishnaswamy (CoRR, 2023)

Bayesian Formulations for Graph Spectral Denoising. - Samuel Leone; Xingzhi Sun 003; Michael Perlmutter; Smita Krishnaswamy (CoRR, 2023)

Understanding Graph Neural Networks with Generalized Geometric Scattering Transforms. - Michael Perlmutter; Alexander Tong 001; Feng Gao; Guy Wolf; Matthew J. Hirn (SIAM J. Math. Data Sci., 2023)

BLIS-Net: Classifying and Analyzing Signals on Graphs. - Charles Xu 004; Laney Goldman; Valentina Guo; Benjamin Hollander-Bodie; Maedee Trank-Greene; Ian Adelstein; Edward De Brouwer; Rex Ying; Smita Krishnaswamy; Michael Perlmutter (AISTATS, 2024)

Bayesian Spectral Graph Denoising with Smoothness Prior. - Samuel Leone; Xingzhi Sun 003; Michael Perlmutter; Smita Krishnaswamy (CISS, 2024)

Directed Scattering for Knowledge Graph-Based Cellular Signaling Analysis. - Aarthi Venkat; Joyce A. Chew; Ferran Cardoso Rodriguez; Christopher J. Tape; Michael Perlmutter; Smita Krishnaswamy (ICASSP, 2024)

Towards a General Recipe for Combinatorial Optimization With Multi-Filter GNNs. - Frederik Wenkel; Semih Cantürk; Stefan Horoi; Michael Perlmutter; Guy Wolf (LoG, 2024)

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning. - Holly R. Steach; Siddharth Viswanath; Yixuan He; Xitong Zhang; Natalia Ivanova; Matthew J. Hirn; Michael Perlmutter; Smita Krishnaswamy (RECOMB, 2024)

Towards a General GNN Framework for Combinatorial Optimization. - Frederik Wenkel; Semih Cantürk; Michael Perlmutter; Guy Wolf (CoRR, 2024)

Convergence of Manifold Filter-Combine Networks. - David R. Johnson; Joyce A. Chew; Siddharth Viswanath; Edward De Brouwer; Deanna Needell; Smita Krishnaswamy; Michael Perlmutter (CoRR, 2024)

ProtSCAPE: Mapping the landscape of protein conformations in molecular dynamics. - Siddharth Viswanath; Dhananjay Bhaskar; David R. Johnson; João F. Rocha; Egbert Castro; Jackson D. Grady; Alex T. Grigas; Michael A. Perlmutter; Corey S. O'Hern; Smita Krishnaswamy (CoRR, 2024)

Learnable Filters for Geometric Scattering Modules. - Alexander Tong 001; Frederik Wenkel; Dhananjay Bhaskar; Kincaid MacDonald; Jackson D. Grady; Michael Perlmutter; Smita Krishnaswamy; Guy Wolf (IEEE Trans. Signal Process., 2024)

Mapping the gene space at single-cell resolution with gene signal pattern analysis. - Aarthi Venkat; Samuel Leone; Scott E. Youlten; Eric Fagerberg; John Attanasio; Nikhil S. Joshi; Michael Perlmutter; Smita Krishnaswamy (Nat. Comput. Sci., 2024)

Hyperedge Representations with Hypergraph Wavelets: Applications to Spatial Transcriptomics. - Xingzhi Sun 003; Charles Xu 004; João F. Rocha; Chen Liu 020; Benjamin Hollander-Bodie; Laney Goldman; Marcello DiStasio; Michael Perlmutter; Smita Krishnaswamy (ICASSP, 2025)

HiPoNet: A Topology-Preserving Multi-View Neural Network For High Dimensional Point Cloud and Single-Cell Data. - Siddharth Viswanath; Hiren Madhu; Dhananjay Bhaskar; Jake Kovalic; David R. Johnson; Rex Ying; Christopher J. Tape; Ian Adelstein; Michael Perlmutter; Smita Krishnaswamy (CoRR, 2025)

InfoGain Wavelets: Furthering the Design of Diffusion Wavelets for Graph-Structured Data. - David R. Johnson; Smita Krishnaswamy; Michael Perlmutter (CoRR, 2025)

Equivariant Geometric Scattering Networks via Vector Diffusion Wavelets. - David R. Johnson; Rishabh Anand; Smita Krishnaswamy; Michael Perlmutter (CoRR, 2025)