Accurate Reliability Evaluation and Enhancement via Probabilistic Transfer Matrices. - Smita Krishnaswamy; George F. Viamontes; Igor L. Markov; John P. Hayes (DATE, 2005)

Logic circuit testing for transient faults. - Smita Krishnaswamy; Igor L. Markov; John P. Hayes (ETS, 2005)

Enhancing design robustness with reliability-aware resynthesis and logic simulation. - Smita Krishnaswamy; Stephen Plaza; Igor L. Markov; John P. Hayes (ICCAD, 2007)

Tracking Uncertainty with Probabilistic Logic Circuit Testing. - Smita Krishnaswamy; Igor L. Markov; John P. Hayes (IEEE Des. Test Comput., 2007)

On the role of timing masking in reliable logic circuit design. - Smita Krishnaswamy; Igor L. Markov; John P. Hayes (DAC, 2008)

Probabilistic transfer matrices in symbolic reliability analysis of logic circuits. - Smita Krishnaswamy; George F. Viamontes; Igor L. Markov; John P. Hayes (ACM Trans. Design Autom. Electr. Syst., 2008)

Design, Analysis and Test of Logic Circuits under Uncertainty. - Smita Krishnaswamy (2008)

Improving testability and soft-error resilience through retiming. - Smita Krishnaswamy; Igor L. Markov; John P. Hayes (DAC, 2009)

DeltaSyn: An efficient logic difference optimizer for ECO synthesis. - Smita Krishnaswamy; Haoxing Ren; Nilesh Modi; Ruchir Puri (ICCAD, 2009)

Signature-Based SER Analysis and Design of Logic Circuits. - Smita Krishnaswamy; Stephen Plaza; Igor L. Markov; John P. Hayes (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2009)

SPIRE: A retiming-based physical-synthesis transformation system. - David A. Papa; Smita Krishnaswamy; Igor L. Markov (ICCAD, 2010)

Joint DAC/IWBDA special session design and synthesis of biological circuits. - Douglas Densmore; Mark Horowitz; Smita Krishnaswamy; Xiling Shen; Adam P. Arkin; Erik Winfree; Chris Voigt (DAC, 2011)

Generalized SAT-sweeping for post-mapping optimization. - Tobias Welp; Smita Krishnaswamy; Andreas Kuehlmann (DAC, 2012)

Can CAD cure cancer? - Smita Krishnaswamy; Bernd Bodenmiller; Dana Pe'er (DAC, 2013)

Intuitive ECO synthesis for high performance circuits. - Haoxing Ren; Ruchir Puri; Lakshmi N. Reddy; Smita Krishnaswamy; Cindy Washburn; Joel Earl; Joachim Keinert (DATE, 2013)

Design, Analysis and Test of Logic Circuits Under Uncertainty - Smita Krishnaswamy; Igor L. Markov; John P. Hayes (Springer, 2013)

MAGAN: Aligning Biological Manifolds. - Matthew Amodio; Smita Krishnaswamy (ICML, 2018)

Geometry Based Data Generation. - Ofir Lindenbaum; Jay S. Stanley III; Guy Wolf; Smita Krishnaswamy (NeurIPS, 2018)

Modeling Dynamics with Deep Transition-Learning Networks. - David van Dijk; Scott Gigante; Alexander Strzalkowski; Guy Wolf; Smita Krishnaswamy (CoRR, 2018)

Manifold Alignment with Feature Correspondence. - Jay S. Stanley III; Guy Wolf; Smita Krishnaswamy (CoRR, 2018)

Graph Spectral Regularization for Neural Network Interpretability. - Alexander Tong 001; David van Dijk; Jay S. Stanley III; Matthew Amodio; Guy Wolf; Smita Krishnaswamy (CoRR, 2018)

Coarse Graining of Data via Inhomogeneous Diffusion Condensation. - Nathan Brugnone; Smita Krishnaswamy; Alex Gonopolskiy; Mark W. Moyle; Manik Kuchroo; David van Dijk; Kevin R. Moon; Daniel Colón-Ramos; Guy Wolf; Matthew J. Hirn (IEEE BigData, 2019)

Finding Archetypal Spaces Using Neural Networks. - David van Dijk; Daniel B. Burkhardt; Matthew Amodio; Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (IEEE BigData, 2019)

TraVeLGAN: Image-To-Image Translation by Transformation Vector Learning. - Matthew Amodio; Smita Krishnaswamy (CVPR, 2019)

Vertex-Frequency Clustering. - Daniel B. Burkhardt; Jay S. Stanley III; Guy Wolf; Smita Krishnaswamy (DSW, 2019)

Visualizing the PHATE of Neural Networks. - Scott Gigante; Adam S. Charles; Smita Krishnaswamy; Gal Mishne (NeurIPS, 2019)

Generating and Aligning from Data Geometries with Generative Adversarial Networks. - Matthew Amodio; Smita Krishnaswamy (CoRR, 2019)

Finding Archetypal Spaces for Data Using Neural Networks. - David van Dijk; Daniel B. Burkhardt; Matthew Amodio; Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (CoRR, 2019)

Compressed Diffusion. - Scott Gigante; Jay S. Stanley III; Ngan Vu; David van Dijk; Kevin R. Moon; Guy Wolf; Smita Krishnaswamy (CoRR, 2019)

A Lipschitz-constrained anomaly discriminator framework. - Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (CoRR, 2019)

Uncovering the Folding Landscape of RNA Secondary Structure Using Deep Graph Embeddings. - Egbert Castro; Andrew Benz; Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (IEEE BigData, 2020)

TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. - Alexander Tong 001; Jessie Huang; Guy Wolf; David van Dijk; Smita Krishnaswamy (ICML, 2020)

Interpretable Neuron Structuring with Graph Spectral Regularization. - Alexander Tong 001; David van Dijk; Jay S. Stanley III; Matthew Amodio; Kristina Yim; Rebecca Muhle; James Noonan; Guy Wolf; Smita Krishnaswamy (IDA, 2020)

Learning General Transformations of Data for Out-of-Sample Extensions. - Matthew Amodio; David van Dijk; Guy Wolf; Smita Krishnaswamy (MLSP, 2020)

Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence. - Bastian Rieck; Tristan Yates; Christian Bock; Karsten M. Borgwardt; Guy Wolf; Nicholas B. Turk-Browne; Smita Krishnaswamy (NeurIPS, 2020)

Harmonic Alignment. - Jay S. Stanley III; Scott Gigante; Guy Wolf; Smita Krishnaswamy (SDM, 2020)

Making Logic Learnable With Neural Networks. - Tobias Brudermueller; Dennis L. Shung; Loren Laine; Adrian J. Stanley; Stig B. Laursen; Harry R. Dalton; Jeffrey Ngu; Michael Schultz; Johannes Stegmaier; Smita Krishnaswamy (CoRR, 2020)

Uncovering the Folding Landscape of RNA Secondary Structure with Deep Graph Embeddings. - Egbert Castro; Andrew Benz; Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (CoRR, 2020)

Image-to-image Mapping with Many Domains by Sparse Attribute Transfer. - Matthew Amodio; Rim Assouel; Victor Schmidt; Tristan Sylvain; Smita Krishnaswamy; Yoshua Bengio (CoRR, 2020)

Data-Driven Learning of Geometric Scattering Networks. - Alexander Tong 001; Frederik Wenkel; Kincaid MacDonald; Smita Krishnaswamy; Guy Wolf (CoRR, 2020)

MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data. - Michal Gerasimiuk; Dennis L. Shung; Alexander Tong 001; Adrian J. Stanley; Michael Schultz; Jeffrey Ngu; Loren Laine; Guy Wolf; Smita Krishnaswamy (IEEE BigData, 2021)

Diffusion Earth Mover's Distance and Distribution Embeddings. - Alexander Tong 001; Guillaume Huguet; Amine Natik; Kincaid MacDonald; Manik Kuchroo; Ronald R. Coifman; Guy Wolf; Smita Krishnaswamy (ICML, 2021)

Multiple-manifold Generation with an Ensemble GAN and Learned Noise Prior. - Matthew Amodio; Smita Krishnaswamy (IDA, 2021)

Noise Space Optimization for GANs. - Matthew Amodio; Smita Krishnaswamy (IJCNN, 2021)

Canvas GAN: Bootstrapped Image-Conditional Models. - Matthew Amodio; Smita Krishnaswamy (IJCNN, 2021)

Multimodal Data Visualization and Denoising with Integrated Diffusion. - Manik Kuchroo; Abhinav Godavarthi; Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (MLSP, 2021)

Data-Driven Learning of Geometric Scattering Modules for GNNs. - Alexander Tong 001; Frederick Wenkel; Kincaid MacDonald; Smita Krishnaswamy; Guy Wolf (MLSP, 2021)

Multimodal single cell data integration challenge: Results and lessons learned. - Christopher Lance; Malte D. Lücken; Daniel B. Burkhardt; Robrecht Cannoodt; Pia Rautenstrauch; Anna Laddach; Aidyn Ubingazhibov; Zhi-Jie Cao; Kaiwen Deng; Sumeer Khan; Qiao Liu 008; Nikolay Russkikh; et al. (NeurIPS (Competition and Demos), 2021)

A sandbox for prediction and integration of DNA, RNA, and proteins in single cells. - Malte Lücken; Daniel Burkhardt; Robrecht Cannoodt; Christopher Lance; Aditi Agrawal; Hananeh Aliee; Ann Chen; Louise Deconinck; Angela Detweiler; Alejandro Granados; Shelly Huynh; Laura Isacco; et al. (NeurIPS Datasets and Benchmarks, 2021)

Visualizing High-Dimensional Trajectories on the Loss-Landscape of ANNs. - Stefan Horoi; Jessie Huang; Guy Wolf; Smita Krishnaswamy (CoRR, 2021)

Multimodal data visualization, denoising and clustering with integrated diffusion. - Manik Kuchroo; Abhinav Godavarthi; Guy Wolf; Smita Krishnaswamy (CoRR, 2021)

Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance. - Alexander Tong 001; Guillaume Huguet; Dennis L. Shung; Amine Natik; Manik Kuchroo; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy (CoRR, 2021)

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

Generating hard-to-obtain information from easy-to-obtain information: Applications in drug discovery and clinical inference. - Matthew Amodio; Dennis L. Shung; Daniel B. Burkhardt; Patrick Wong; Michael Simonov; Yu Yamamoto; David van Dijk; Francis Perry Wilson; Akiko Iwasaki; Smita Krishnaswamy (Patterns, 2021)

Embedding Signals on Graphs with Unbalanced Diffusion Earth Mover's Distance. - Alexander Tong 001; Guillaume Huguet; Dennis L. Shung; Amine Natik; Manik Kuchroo; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy (ICASSP, 2022)

Exploring the Geometry and Topology of Neural Network Loss Landscapes. - Stefan Horoi; Jessie Huang; Bastian Rieck; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy (IDA, 2022)

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

Learning Shared Neural Manifolds from Multi-Subject FMRI Data. - Jessie Huang; Erica L. Busch; Tom Wallenstein; Michal Gerasimiuk; Andrew Benz; Guillaume Lajoie; Guy Wolf; Nicholas B. Turk-Browne; Smita Krishnaswamy (MLSP, 2022)

Diffusion Curvature for Estimating Local Curvature in High Dimensional Data. - Dhananjay Bhaskar; Kincaid MacDonald; Oluwadamilola Fasina; Dawson Thomas; Bastian Rieck; Ian Adelstein; Smita Krishnaswamy (NeurIPS, 2022)

Manifold Interpolating Optimal-Transport Flows for Trajectory Inference. - Guillaume Huguet; Daniel Sumner Magruder; Alexander Tong 001; Oluwadamilola Fasina; Manik Kuchroo; Guy Wolf; Smita Krishnaswamy (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)

Guided Generative Protein Design using Regularized Transformers. - Egbert Castro; Abhinav Godavarthi; Julian A. Rubinfien; Kevin B. Givechian; Dhananjay Bhaskar; Smita Krishnaswamy (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)

CUTS: A Fully Unsupervised Framework for Medical Image Segmentation. - Matthew Amodio; Feng Gao; Arman Avesta; Sanjay Aneja; Luciano V. Del Priore; Jay Wang; Smita Krishnaswamy (CoRR, 2022)

Geodesic Sinkhorn: optimal transport for high-dimensional datasets. - Guillaume Huguet; Alexander Tong 001; María Ramos Zapatero; Guy Wolf; Smita Krishnaswamy (CoRR, 2022)

Single-cell multi-modal GAN reveals spatial patterns in single-cell data from triple-negative breast cancer. - Matthew Amodio; Scott E. Youlten; Aarthi Venkat; Beatriz P. San Juan; Christine L. Chaffer; Smita Krishnaswamy (Patterns, 2022)

Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators. - Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (J. Signal Process. Syst., 2022)

Transformer-based protein generation with regularized latent space optimization. - Egbert Castro; Abhinav Godavarthi; Julian A. Rubinfien; Kevin B. Givechian; Dhananjay Bhaskar; Smita Krishnaswamy (Nat. Mac. Intell., 2022)

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

Neural FIM for learning Fisher information metrics from point cloud data. - Oluwadamilola Fasina; Guillaume Huguet; Alexander Tong 001; Yanlei Zhang; Guy Wolf; Maximilian Nickel; Ian Adelstein; Smita Krishnaswamy (ICML, 2023)

Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations. - Dhananjay Bhaskar; Daniel Sumner Magruder; Matheo Morales; Edward De Brouwer; Aarthi Venkat; Frederik Wenkel; Guy Wolf; Smita Krishnaswamy (LoG, 2023)

Geodesic Sinkhorn For Fast and Accurate Optimal Transport on Manifolds. - Guillaume Huguet; Alexander Tong 001; María Ramos Zapatero; Christopher J. Tape; Guy Wolf; Smita Krishnaswamy (MLSP, 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)

A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction. - Guillaume Huguet; Alexander Tong 001; Edward De Brouwer; Yanlei Zhang; Guy Wolf; Ian Adelstein; Smita Krishnaswamy (NeurIPS, 2023)

Graph Fourier MMD for Signals on Graphs. - Samuel Leone; Aarthi Venkat; Guillaume Huguet; Alexander Tong 001; Guy Wolf; Smita Krishnaswamy (CoRR, 2023)

Inferring dynamic regulatory interaction graphs from time series data with perturbations. - Dhananjay Bhaskar; Daniel Sumner Magruder; Edward De Brouwer; Aarthi Venkat; Frederik Wenkel; Guy Wolf; Smita Krishnaswamy (CoRR, 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)

Multi-view manifold learning of human brain-state trajectories. - Erica L. Busch; Jessie Huang; Andrew Benz; Tom Wallenstein; Guillaume Lajoie; Guy Wolf; Smita Krishnaswamy; Nicholas B. Turk-Browne (Nat. Comput. Sci., 2023)

Time-Inhomogeneous Diffusion Geometry and Topology. - Guillaume Huguet; Alexander Tong 001; Bastian Rieck; Jessie Huang; Manik Kuchroo; Matthew J. Hirn; Guy Wolf; Smita Krishnaswamy (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)

Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy. - Danqi Liao; Chen Liu 020; Benjamin W. Christensen; Alexander Tong 001; Guillaume Huguet; Guy Wolf; Maximilian Nickel; Ian Adelstein; 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)

CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation. - Chen Liu 020; Matthew Amodio; Liangbo L. Shen; Feng Gao; Arman Avesta; Sanjay Aneja; Jay C. Wang; Luciano V. Del Priore; Smita Krishnaswamy (MICCAI (8), 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)

ImageFlowNet: Forecasting Multiscale Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images. - Chen Liu 020; Ke Xu 010; Liangbo L. Shen; Guillaume Huguet; Zilong Wang; Alexander Tong 001; Danilo Bzdok; Jay Stewart; Jay C. Wang; Luciano V. Del Priore; Smita Krishnaswamy (CoRR, 2024)

Looking through the mind's eye via multimodal encoder-decoder networks. - Arman Afrasiyabi; Erica L. Busch; Rahul Singh; Dhananjay Bhaskar; Laurent Caplette; Nicholas B. Turk-Browne; Smita Krishnaswamy (CoRR, 2024)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. - Xingzhi Sun 003; Danqi Liao; Kincaid MacDonald; Yanlei Zhang; Chen Liu 020; Guillaume Huguet; Guy Wolf; Ian Adelstein; Tim G. J. Rudner; Smita Krishnaswamy (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)

Exploring the Manifold of Neural Networks Using Diffusion Geometry. - Elliott Abel; Peyton Crevasse; Yvan Grinspan; Selma Mazioud; Folu Ogundipe; Kristof Reimann; Ellie Schueler; Andrew J. Steindl; Ellen Zhang; Dhananjay Bhaskar; Siddharth Viswanath; Yanlei Zhang; et al. (CoRR, 2024)

Low-Dimensional Embeddings of High-Dimensional Data: Algorithms and Applications (Dagstuhl Seminar 24122). - Dmitry Kobak; Fred A. Hamprecht; Smita Krishnaswamy; Gal Mishne; Sebastian Damrich (Dagstuhl Reports, 2024)

StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. - Ho-Joon Lee; Lee H. Schwamm; Lauren H. Sansing; Hooman Kamel; Adam DeHavenon; Ashby C. Turner; Kevin N. Sheth; Smita Krishnaswamy; Cynthia Brandt; Hongyu Zhao 003; Harlan M. Krumholz; Richa Sharma (npj Digit. Medicine, 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)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds. - Xingzhi Sun 003; Danqi Liao; Kincaid MacDonald; Yanlei Zhang; Guillaume Huguet; Guy Wolf; Ian Adelstein; Tim G. J. Rudner; Smita Krishnaswamy (AISTATS, 2025)

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)

Latent Representation Learning for Multimodal Brain Activity Translation. - Arman Afrasiyabi; Dhananjay Bhaskar; Erica L. Busch; Laurent Caplette; Rahul Singh; Guillaume Lajoie; Nicholas B. Turk-Browne; Smita Krishnaswamy (ICASSP, 2025)

DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images. - Chen Liu 020; Danqi Liao; Alejandro Parada-Mayorga; Alejandro Ribeiro; Marcello DiStasio; Smita Krishnaswamy (ICASSP, 2025)

ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images. - Chen Liu 020; Ke Xu 010; Liangbo L. Shen; Guillaume Huguet; Zilong Wang; Alexander Tong 001; Danilo Bzdok; Jay Stewart; Jay C. Wang; Luciano V. Del Priore; Smita Krishnaswamy (ICASSP, 2025)

Principal Curvatures Estimation with Applications to Single Cell Data. - Yanlei Zhang; Lydia Mezrag; Xingzhi Sun 003; Charles Xu 004; Kincaid MacDonald; Dhananjay Bhaskar; Smita Krishnaswamy; Guy Wolf; Bastian Rieck (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)

HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts. - Neil He; Rishabh Anand; Hiren Madhu; Ali Maatouk; Smita Krishnaswamy; Leandros Tassiulas; Menglin Yang 001; Rex Ying (CoRR, 2025)

HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data. - Hiren Madhu; João F. Rocha; Tinglin Huang; Siddharth Viswanath; Smita Krishnaswamy; Rex Ying (CoRR, 2025)

SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics. - Siddharth Viswanath; Rahul Singh; Yanlei Zhang; J. Adam Noah; Joy Hirsch; Smita Krishnaswamy (CoRR, 2025)

STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics. - João F. Rocha; Ke Xu 010; Xingzhi Sun 003; Ananya Krishna; Dhananjay Bhaskar; Blanche Mongeon; Morgan Craig; Mark Gerstein; Smita Krishnaswamy (CoRR, 2025)