“Residual-Stream Burden Shapes Representation Learning in Diffusion Transformers.” Arxiv: 2609.33895, 2026. (with Tongtong Liang, Siqi Kou, Ziqiao Xi, Esha Singh, Kun Zhou, Zhijie Deng, Yu-Xiang Wang, Rahul Parhi)

“Transformers as Cross-Task Learners: Shared Structure Drives Sample Efficiency in In-Context Learning.” Arxiv: 2609.29060, 2026. (with Zhongjie Shi, Rongjie Lai, Wenjing Liao)

“Reassessing Muon for Matrix Factorization.” Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS), 2026. (with Ali Parviz, Gal Mishne)

“GeoEdit: Local Frames for Fast, Training-Free On-Manifold Editing in Diffusion Models.” Arxiv: 2604.24238, 2026. (with Yiming Zhang, Sitong Liu, Ke Li, Zhihong Wu, Melvin Leok)

“Does Sparse Connectivity Improve Generalization? Convolutional Networks Below the Edge of Stability.” Arxiv: 2603.04807, 2026. (with Tongtong Liang, Esha Singh, Rahul Parhi, Yu-Xiang Wang)

“OT Score: An OT based Confidence Score for Prototype-Assisted Source Free Unsupervised Domain Adaptation.” Transactions on Machine Learning Research, 2026. (with Yiming Zhang, Sitong Liu)

“Generalization Below the Edge of Stability: The Role of Data Geometry.” International Conference on Learning Representations (ICLR), 2026. (with Tongtong Liang, Rahul Parhi, Yu-Xiang Wang)

“Revisiting Meta-Learning with Noisy Labels: Reweighting Dynamics and Theoretical Guarantees.” Arxiv: 2510.12209, 2025. (with Yiming Zhang, Chester Holtz, Gal Mishne)

“KAIROS: Scalable Model-Agnostic Data Valuation.” Advances in Neural Information Processing Systems (NeurIPS), 2025. (with Jiongli Zhu, Parjanya Prashant, Babak Salimi)

“Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights.” Arxiv: 2505.03205, 2025. (with Zhaiming Shen, Alex Havrilla, Rongjie Lai, Wenjing Liao)

“Training Guarantees of Neural Network Classification Two-Sample Tests by Kernel Analysis.” Arxiv: 2407.04806, 2024. (with Varun Khurana, Xiuyuan Cheng)

“Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors.” ICML Workshop on Topological, Algebraic and Geometric Learning, 2022. (with Chester Holtz, Gal Mishne)

“Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders.” Arxiv: 2208.10570, 2022. (with Stefan C Schonsheck, Scott Mahan, Timo Klock, Rongjie Lai)

“Nonclosedness of the Set of Neural Networks in Sobolev Space.” Neural Networks, 2021. (with Scott Mahan, Emily J. King)

“A deep network construction that adapts to intrinsic dimensionality beyond the domain.” Neural Networks, 2021. (with Timo Klock)

“Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks.” International Conference on Machine Learning (ICML), 2020. (with Ahmed Taha Elthakeb, Prannoy Pilligundla, Fatemeh Mireshghallah, Hadi Esmaeilzadeh)

“Variational Random Walk Autoencoders.” The European Conference on Computer Vision (ECCV), 2020. (with Henry Li, Ofir Lindenbaum, Xiuyuan Cheng)

“PT-MMD: A novel statistical framework for the evaluation of generative systems.” Asilomar Conference on Signals, Systems, and Computers, 2019. (with Alexander Potapov, Ian Colbert, Dhiman Sengupta, Ken Kreutz-Delgado, Srinjoy Das)

“Defending against adversarial images using basis functions transformations.” Arxiv: 1803.10840, 2018. (with Uri Shaham, James Garritano, Yutaro Yamada, Ethan Weinberger, Xiuyuan Cheng, Kelly Stanton, Yuval Kluger)

“Provable approximation properties for deep neural networks.” Applied and Computational Harmonic Analysis, 2017. (with Uri Shaham, Raphy Coifman)

“Diffusion Nets.” Applied and Computational Harmonic Analysis, 2017. (with Gal Mishne, Uri Shaham, Israel Cohen)

“Bigeometric organization of deep nets.” Applied and Computational Harmonic Analysis, 2017. (with Nicholas Downing, Harlan Krumholz, Raphy Coifman)

“Riemannian ascent–descent for nonconvex nonconcave minimax landscapes: convergence to basin saddle points and applications to distributionally robust optimization.” Arxiv: 2609.14141, 2026. (with Rishabh Dixit, Pranav Upadrashta)

“Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems.” SIAM Dynamical Systems, 2026. (with Nicholas Karris, Evangelos A Nikitopoulos, Ioannis G Kevrekidis, Seungjoon Lee)

“Optimal Transport, Timesteppers, Newton-Krylov Methods and Steady States of Collective Particle Dynamics.” Arxiv: 2512.24567, 2025. (with Hannes Vandecasteele, Nicholas Karris, Ioannis G Kevrekidis)

“Linearized Optimal Transport for Analysis of High-Dimensional Point-Cloud and Single-Cell Data.” Arxiv: 2510.22033, 2025. (with Tianxiang Wang, Yingtong Ke, Dhananjay Bhaskar, Smita Krishnaswamy)

“Unbalanced Optimal Transport Dictionary Learning for Unsupervised Hyperspectral Image Clustering.” IEEE Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2025. (with Joshua Lentz, Nicholas Karris, James M Murphy)

“Linearized Optimal Transport pyLOT Library: A Toolkit for Machine Learning on Point Clouds.” Arxiv: 2502.03439, 2025. (with Jun Linwu, Varun Khurana, Nicholas Karris)

“Linearized Wasserstein dimensionality reduction with approximation guarantees.” Applied and Computational Harmonic Analysis, 2025. (with Keaton Hamm, Varun Khurana, Caroline Moosmüller)

“Resistance Distance and Linearized Optimal Transport on Graphs.” Arxiv: 2404.15261, 2024. (with Sawyer Robertson, Zhengchao Wan)

“OTClean: Data Cleaning for Conditional Independence Violations using Optimal Transport.” Proceedings of the ACM on Management of Data, 2024. (with Alireza Pirhadi, Mohammad Hossein Moslemi, Mostafa Milani, Babak Salimi)

“Point Cloud Classification via Deep Set Linearized Optimal Transport.” Arxiv: 2401.01460, 2023. (with Scott Mahan, Caroline Moosmüller)

“Supervised learning of sheared distributions using linearized optimal transport.” Sampling Theory, Signal Processing, and Data Analysis, 2023. (with Varun Khurana, Harish Kannan, Caroline Moosmüller)

“Linear Optimal Transport Embedding: Provable fast Wasserstein distance computation and classification for nonlinear problems.” Information and Inference, 2022. (with Caroline Moosmüller)

“Classification Logit Two-sample Testing by Neural Networks.” IEEE Transactions on Information Theory, 2022. (with Xiuyuan Cheng)

“Kernel distance measures for time series, random fields and other structured data.” Frontiers in Applied Mathematics and Statistics-Mathematics of Computation and Data Science, 2021. (with Srinjoy Das, Hrushikesh Mhaskar)

“Cautious Active Clustering.” Applied and Computational Harmonic Analysis, 2021. (with Hrushikesh Mhaskar)

“A witness function based construction of discriminative models using Hermite polynomials.” Frontiers in Applied Mathematics and Statistics-Mathematics of Computation and Data Science, 2020. (with Hrushikesh Mhaskar, Xiuyuan Cheng)

“Bounding the Error From Reference Set Kernel Maximum Mean Discrepancy.” Arxiv: 1812.04594, 2018.

“Two-sample statistics based on anisotropic kernels.” Information and Inference, 2019. (with Xiuyuan Cheng, Raphy Coifman)

“People mover’s distance: Class level geometry using fast pairwise data adaptive transportation costs.” Applied and Computational Harmonic Analysis, 2018. (with Brita Roy, Carley Riley, Harlan Krumholz)

“Scalable Graph Coreset Selection via Greedy Sampling.” Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS), 2026. (with Zhaiming Shen)

“RATS: Unsupervised manifold learning using low-distortion alignment of tangent spaces.” Nature Neuroscience, 2026. (with Dhruv Kohli, Johannes S Nieuwenhuis, Gal Mishne, Devika Narain)

“Non-degenerate Rigid Alignment in a Patch Framework.” SIAM Journal on Optimization, 2026. (with Dhruv Kohli, Gal Mishne)

“Precision Matrix based Feature Learning Mechanism for Subspace Clustering Task.” Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS), 2026. (with Haohan Zou)

“Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization.” Arxiv: 2510.02308, 2025. (with Dhruv Kohli, Sawyer Jack Robertson, Gal Mishne)

“On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs.” SIAM Journal on Scientific Computing, 2025. (with Sawyer Jack Robertson, Dhruv Kohli, Gal Mishne)

“LINSCAN – A Linearity Based Clustering Algorithm.” Conference on Topology, Algebra, and Geometry in Data Science (TAG-DS), 2025. (with Andrew Dennehy, Xiaoyu Zou, Shabnam Semnani, Yuri Fialko)

“Robust Graph-Based Semi-Supervised Learning via p-Conductances.” Arxiv: 2502.08873, 2025. (with Sawyer Jack Robertson, Chester Holtz, Zhengchao Wan, Gal Mishne)

“Robust boundary detection and density estimation using doubly stochastic scaling of the Gaussian kernel.” Arxiv: 2411.18942, 2024. (with Dhruv Kohli, Jesse He, Chester Holtz, Gal Mishne)

“Random Walks, Conductance, and Resistance for the Connection Graph Laplacian.” SIAM Journal on Matrix Analysis and Applications, 2024. (with Gal Mishne, Andreas Oslandsbotn, Sawyer Jack Robertson, Zhengchao Wan, Yusu Wang)

“Semi-Supervised Laplacian Learning on Stiefel Manifolds.” Arxiv: 2308.00142, 2023. (with Chester Holtz, Pengwen Chen, Chung-Kuan Cheng, Gal Mishne)

“Effective resistance in metric spaces.” Arxiv: 2306.15649, 2023. (with Robi Bhattacharjee, Andreas Oslandsbotn, Yoav Freund)

“Structure from Voltage.” Arxiv: 2203.00063, 2022. (with Robi Bhattacharjee, Andreas Oslandsbotn, Yoav Freund)

“StreaMRAK: a Streaming Multi-Resolution Adaptive Kernel Algorithm.” Applied Mathematics and Computation, 2021. (with Andreas Oslandsbotn, Zeljko Kereta, Valeriya Naumova, Yoav Freund)

“LDLE: Low Distortion Local Eigenmaps.” Journal of Machine Learning Research, 2021. (with Dhruv Kohli, Gal Mishne)

“Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples.” Proceedings of Uncertainty in Artificial Intelligence (UAI), 2020. (with Saeed Vahidian, Baharan Mirzasoleiman)

“The pre-image problem for Laplacian Eigenmaps utilizing L1 regularization with applications to data fusion.” Inverse Problems, 2017. (with Tim Doster, Wojciech Czaja)

“Prediction models for graph-linked data with localized regression.” SPIE, 2017.

“A note on Markov normalized magnetic eigenmaps.” Applied and Computational Harmonic Analysis, 2017.

“Spectral echolocation via the wave embedding.” Applied and Computational Harmonic Analysis, 2017. (with Stefan Steinerberger)

“Function Driven Diffusion for Personalized Counterfactual Inference.” Arxiv: 1610.10025, 2017.

“Eigenvector localization on data-dependent graphs.” International Conference on Sampling Theory and Applications (SampTA), 2015. (with Wojciech Czaja)

“Multimodal Data Fusion via Graph-and Operator-based Techniques.” Hyperspectral Imaging and Sounding of the Environment, 2015. (with Tim Doster, Wojciech Czaja)

“Operator analysis and diffusion based embeddings for heterogeneous data fusion.” IEEE Geoscience and Remote Sensing Symposium, 2014. (with Tim Doster, Wojciech Czaja)

“Operator based integration of information in multimodal radiological search mission with applications to anomaly detection.” SPIE, 2014. (with John Benedetto, Wojciech Czaja, Tim Doster, Kevin Kochersberger, Ben Manning, Tom McCullough, Lance McLane)

“LIDAR image recovery by incorporating heterogeneous imaging modalities.” SPIE, 2014. (with Wojciech Czaja)

“A case study on data fusion with overlapping segments.” IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2013. (with Tim Doster, Wojciech Czaja)

“Spectral Bounds for Kernel Quadrature.” Arxiv: 2609.00553, 2026. (with Quoc Thong Le Gia, Hrushikesh Mhaskar)

“Sigma-Delta and Distributed Noise-Shaping Quantization Methods for Random Fourier Features.” Information and Inference, 2024. (with Jinjie Zhang, Rayan Saab, Harish Kannan)

“A low discrepancy sequence on graphs.” Journal of Fourier Analysis and Applications, 2021. (with Hrushikesh Mhaskar)

“Natural Graph Wavelet Packet Dictionaries.” Journal of Fourier Analysis and Applications, 2020. (with Haotian Li, Naoki Saito)

“On the Dual Geometry of Laplacian Eigenfunctions.” Experimental Mathematics, 2019. (with Stefan Steinerberger)

“On suprema of autoconvolutions with an application to Sidon sets.” Proceedings of the American Mathematical Society, 2017. (with Stefan Steinerberger)

“Solving 2D Fredholm integral from incomplete measurements using compressive sensing.” SIAM Journal on Imaging Sciences, 2014. (with Ruiliang Bai, Peter Basser, Wojciech Czaja)

“Using Causal Inference to Explore Government Policy Impact on Computer Usage.” Arxiv: 2503.09957, 2025. (with Mingjia Zhu, Lechuan Wang, Julien Sebot, Bijan Arbab, Babak Salimi)

“A neural network kernel decomposition for learning multiple steady states in parameterized dynamical systems.” Arxiv: 2312.10315, 2023. (with Yimeng Zhang, Bo Li, Xiaochuan Tian)

“High‐Angle Active Conjugate Faults in the Anza‐Borrego Shear Zone, Southern California.” Geophysical Research Letters, 2023. (with Xiaoyu Zou, Yuri Fialko, Andrew Dennehy, Shabnam Semnani)

“Identifying drug effects in a cardiac model of electrophysiology using kernel-based parameter estimation methods.” BioRxiv: 2023.03.15.532862, 2023. (with Andreas Oslandsbotn, Nickolas Forsch)

“Using Neural Networks to Predict Micro-Spatial Economic Growth.” American Economic Review: Insights, 2022. (with Arman Khachiyan, Anthony Thomas, Huye Zhou, Gordon H Hanson, Tajana Rosing, Amit Khandelwal)

“A Manifold Learning Based Video Prediction Approach for Deep Motion Transfer.” International Conference on Computer Vision (ICCV), 2021. (with Yuliang Cai, Sumit Mohan, Adithya Niranjan, Nilesh Jain, Srinjoy Das)

“Magnetic resonance 2D relaxometry reconstruction using partial data.” US Patent No. 10802098, 2020. (with Peter J Basser, Ruiliang Bai, Wojciech Czaja)

“DeepSurv: Personalized treatment recommender system using A Cox proportional hazards deep neural network.” BMC Medical Research Methodology, 2018. (with Jared Katzman, Uri Shaham, Jonathan Bates, Tingting Jiang, Yuval Kluger)

“Outcome Based Matching.” Arxiv: 1712.05063, 2018. (with Jonathan Bates)

“Describing the performance of US hospitals by applying big data analytics.” PloS One, 2017. (with Nicholas Downing, Arjun Venkatesh, Angela Hsieh, Elizabeth Drye, Raphy Coifman, Harlan Krumholz)

“2D sparse sampling algorithm for ND Fredholm equations with applications to NMR relaxometry.” International Conference on Sampling Theory and Applications (SampTA), 2015. (with Ariel Hafftka, Hasan Celik, Wojciech Czaja, Richard Spencer)

“Efficient 2D MRI relaxometry using compressed sensing.” Journal of Magnetic Resonance, 2015. (with Ruiliang Bai, Wojciech Czaja, Peter Basser)

“Hildreth’s algorithm with applications to soft constraints for user interface layout.” Journal of Computational and Applied Mathematics, 2015. (with Noreen Jamil, Xuemei Chen)

“Examining potential residential participation in financial incentives to mitigate impervious surface effects in Howard County, Maryland.” Suburban Sustainability, 2014. (with Kristin M Larson, Jim Caldwell)

“A general formula for reactant conversion over a single catalyst particle in TAP pulse experiments.” Chemical Engineering Science, 2009. (with Renato Feres, Grigoriy Yablonsky, John Gleaves)