Publications

Submitted / preprints

• RATS: Unsupervised manifold learning using low-distortion alignment of tangent spaces
  D. Kohli*, J. S. Nieuwenhuis*, A. Cloninger, G. Mishne#, and D. Narain# [bioRxiv]


• Robust estimation of boundary using doubly stochastic scaling of Gaussian kernel
  D. Kohli, J. He, C. Holtz, G. Mishne and A. Cloninger [arXiv]


• Characterizing Behavioral Dynamics in Bipolar Disorder with Computational Ethology
  Z. Zhang, C. Chou, H. Rosberg, W. Perry, J. W. Young, Ap. Minassian, G. Mishne#, and M. Aoi# [medrxiv]


• Tree-Wasserstein Distance for High Dimensional Data with a Latent Feature Hierarchy
  Y-W. Lin, R. R. Coifman, G. Mishne and R Talmon [arxiv]


• Neurovascular Impulse Response Function (IRF) during spontaneous activity differentially reflects intrinsic neuromodulation across cortical regions
  B. C. Rauscher*, N. Fomin-Thunemann*, S. Kura*, P. R. Doran* et al. [bioRxiv]


• Multiway Multislice PHATE: Visualizing Hidden Dynamics of RNNs through Training
  J. Xie, L. C. K. Voinov, N. Mudrik, G.Mishne and A. Charles [arXiv]


• On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs
  S. J. Robertson, D. Kohli, G. Mishne and A. Cloninger [arXiv]


• Semi-Supervised Laplacian Learning on Stiefel Manifolds
  C. Holtz, P. Chen, A. Cloninger, C-K. Cheng and G. Mishne [arXiv]


• Graph Laplacian Learning with Exponential Family Noise
  C. Shi and G. Mishne [arXiv]


• Non-degenerate Rigid Alignment in a Patch Framework
  D. Kohli, G. Mishne and A. Cloninger [arXiv]


• Learning Sample Reweighting for Accuracy and Adversarial Robustness
  C. Holtz, T-W. Weng and G. Mishne [arXiv]


• Provable Robustness by Geometric Regularization of ReLU Networks
  C. Holtz, C. Shi and G. Mishne
  


• Automated cellular structure extraction in biological images with applications to calcium imaging data
  G. Mishne, R. R. Coifman, M. Lavzin and J. Schiller
  [bioRxiv]

Journal Publications

• Better together: A systematic review of studies combining magnetic resonance imaging with ecological momentary assessment
  R. Gadassi-Polack, G. Paganini, J. Winschel, H. Benisty, J. Joormann, H. Kober and G. Mishne
  Social Neuroscience, 2024 [psyarxiv]


• Random Walks, Conductance, and Resistance for the Connection Graph Laplacian
  A. Cloninger, G. Mishne, A. Oslandsbotn, S. J. Robertson, Z. Wan and Y. Wang
  SIAM Journal on Matrix Analysis and Applications, 2024 [arXiv]


• Long-wavelength traveling waves of vasomotion modulate the perfusion of cortex,
  T. Broggini*, J. Duckworth*, X. Ji#, R. Liu#, X. Xia#, P. Mächler, I. Shaked, L. P. Munting, S. Iyengar, M. Kotlikoff, S. J. van Veluw, M. Vergassola, G. Mishne and D. Kleinfeld,
  Neuron, 2024


• Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior
  H. Benisty, D. Barson, A. H. Moberly, S. Lohani, L. Tang, R. R. Coifman, M. Crair, G. Mishne, J. A. Cardin and M. J. Higley
  Nature Neuroscience, 2023 [bioRxiv]


• Data Processing of Functional Optical Microscopy for Neuroscience
  H. Benisty, A. Song, G. and A. S. Charles
  SPIE Neurophotonics, 2022. [arXiv]


• Which Features of Repetitive Negative Thinking and Positive Reappraisal Predict Depression? An In-Depth Investigation using Artificial Neural Networks with Feature Selection
  J. Everaert*, H. Benisty*, R. Gadassi-Polack, J. Joormann, and G. Mishne
  Journal of Psychopathology and Clinical Science, 2022.


• GraFT: Graph Filtered Temporal Dictionary Learning for Functional Neural Imaging
  A. S. Charles, N. Cermak, R. Affan, B. Scott, J. Schiller and G. Mishne
  IEEE Transactions on Image Processing, May 2022. [code]


• Applications and comparison of dimensionality reduction methods for microbiome data
  G. W. Armstrong, G. Rahman, C. Martino, D. Mcdonald, A. Gonzalez, G. Mishne and R. Knight
  Frontiers in Bioinformatics, 2022.


• LDLE: Low Distortion Local Eigenmaps
  D. Kohli, A. Cloninger and G. Mishne
  JMLR, 2021. [arXiv] [code]


• UMAP reveals composite patterns and resolves visualization artifacts in microbiome data
  G. Armstrong, C. Martino, G. Rahman, A. Gonzalez , Y. Vázquez-Baeza, G. Mishne and R. Knight
  mSystems, Oct. 2021. [code]


• Multi-scale Affinities with Missing Data: Estimation and Applications
  M. Zhang, G. Mishne and E. C. Chi
  Statistical Analysis and Data Mining, Nov. 2021.


• Smooth graph learning for functional connectivity estimation
  S. Gao*, X. Xia*, D. Scheinost and G. Mishne
  NeuroImage, Oct. 2021. [code]


• Non-linear manifold learning in fMRI uncovers a low-dimensional space of brain dynamics
  S. Gao, G. Mishne and D. Scheinost
  Human Brain Mapping, June 2021. [biorxiv] [code]


• COBRAC: a fast implementation of convex biclustering with compression
  H. Yi, L. Huang, G. Mishne and E. C. Chi
  Bioinformatics, 2021. [code], [website]


• Kernel-based parameter estimation of dynamical systems with unknown observation functions
  O. Lindenbaum*, A. Sagiv*, G. Mishne and R. Talmon
  Chaos: An Interdisciplinary Journal of Nonlinear Science 31, 043118, 2021. [arXiv]


• Multi-way Graph Signal Processing on Tensors: Integrative analysis of irregular geometries
  J. S. Stanley, E. C. Chi and G. Mishne
  IEEE Signal Processing Magazine, vol. 37, no. 6, pp. 160-173, Nov. 2020. [arXiv]


• Spectral embedding norm: looking deep into the spectrum of the graph Laplacian
  X. Cheng and G. Mishne
  SIAM Journal on Imaging Sciences, 2020, 13(2), 1015–1048. [arXiv]


• Randomized Near Neighbor Graphs, Giant Components, and Applications in Data Science
  A. Jaffe, Y. Kluger, G. C. Linderman, G. Mishne and S. Steinerberger
  Journal of Applied Probability, 57(2), 458-476, June 2020. [arXiv]


• The Geometry of Nodal Sets and Outlier Detection
  X. Cheng, G. Mishne and S. Steinerberger
  Journal of Number Theory, Oct. 2017. [code]


• Diffusion Nets
  G. Mishne, U. Shaham, A. Cloninger and I. Cohen
  Applied and Computational Harmonic Analysis, Aug. 2017. [code]


• Data-driven tree transforms and metrics
  G. Mishne, R. Talmon, I. Cohen, Y. Kluger and R. R. Coifman
  IEEE Transactions on Signal and Information Processing over Networks, Aug. 2017. [python] [Matlab]


• Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discovery
  G. Mishne, R. Talmon, R. Meir, J. Schiller, U. Dubin and R. R. Coifman
  Special Issue of IEEE Journal of Selected Topics in Signal Processing on Advanced Signal Processing in Brain Networks, vol. 10, no. 7, pp. 1238-1253, Oct. 2016. [code]


• Graph-Based Supervised Automatic Target Detection
  G. Mishne, R. Talmon and I. Cohen
  IEEE Trans. Geoscience and Remote Sensing, Vol. 53, Number 5, May 2015, pp. 2738-2754.


• Multiscale Anomaly Detection Using Diffusion Maps
  G. Mishne and I. Cohen
  Special Issue of IEEE Journal of Selected Topics in Signal Processing on Anomalous Pattern Discovery for Spatial, Temporal, Networked, and High-Dimensional Signals, Vol. 7, Number 1, Feb. 2013, pp. 111-123. [code]

Conference and Workshop Proceedings

• Continuous Partitioning for Graph-Based Semi-Supervised Learning
  C. Holtz, P. Chen, Z. Wan, C-K. Cheng and G. Mishne
  NeurIPS-2024.


• Comparing Graph Transformers via Positional Encodings
  M. Black, Z. Wan, G. Mishne, A. Nayyeri, and Y. Wang
  ICML-2024 [arXiv]


• Contextual Feature Selection with Conditional Stochastic Gates
  R. D. Sristi, O. Lindenbaum, M. Lavzin, J. Schiller, G. Mishne and H. Benisty
  ICML-2024 [arXiv]


• SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
  N. Mudrik, G. Mishne and A. Charles
  ICML-2024 [arXiv]


• Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error
  J. Huang, P. Tostado-Marcos, S. Manojna Narasimha, A. N. Patel, E. M. Arneodo, T. Q. Gentner, G. Mishne and V. Gilja
  IEEE EMBC-2024


• Learning Cartesian Product Graphs with Laplacian Constraints
  C. Shi and G. Mishne
  AISTATS-2024 [arXiv]


• Deep and shallow data science for multi-scale optical neuroscience
  G. Mishne and A. Charles
  SPIE Neural Imaging and Sensing 2024 [arXiv]


• Product Manifold Learning with Independent Coordinate Selection
  J. He, T. Brugère and G. Mishne
  ICML workshop on Topology, Algebra, and Geometry in Machine Learning, 2023.


• Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
  Y-W. Lin, R.R. Coifman, G. Mishne and R. Talmon
  ICML-2023 [arXiv]


• The Numerical Stability of Hyperbolic Representation Learning
  G. Mishne, Z. Wan, Y. Wang and S. Yang
  ICML-2023 [arXiv]


• Implicit Graphon Neural Representation
  X. Xia, G. Mishne and Y. Wang
  AISTATS-2023 (oral). [code]


• DiSC: Differential Spectral Clustering of Features
  D. Sristi, G. Mishne and A. Jaffe
  NeurIPS-2022.


• Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors
  C. Holtz, G. Mishne and A. Cloninger
  ICML Workshop on Topological, Algebraic, and Geometric Learning


• Learning Disentangled Behavior Embeddings
  C. Shi, S. Schwartz, S. Levy, S. Achvat, M. Abboud, A. Ghanayim, J. Schiller and G. Mishne
  NeurIPS-2021 (Spotlight). [code]


• LDLE: Low Distortion Local Eigenmaps
  D. Kohli, A. Cloninger and G. Mishne
  ICLR 2021 Workshop on Geometrical and Topological Representation Learning


• Scalable Algorithms for Convex Clustering
  W. Zhou, H. Yi, G. Mishne and E. C. Chi
  IEEE Data Science and Learning Workshop (DSLW-2021).


• Online Adversarial Purification based on Self-supervised Learning
  C. Shi, C. Holtz and G. Mishne
  ICLR-2021. [code]


• Poincaré embedding reveals edge-based functional networks of the brain
  S. Gao, G. Mishne and D. Scheinost
  MICCAI-2020, pp. 448-457, Online, October 2020.


• Visualizing the PHATE of Neural Networks
  S. Gigante, A. S. Charles, S. Krishnaswamy and G. Mishne
  NeurIPS 2019, Vancouver, December 2019. [arXiv] [code]


• Co-manifold learning with missing data
  G. Mishne, E. C. Chi, R. R. Coifman
  ICML 2019, Long Beach, CA, June 2019. [arXiv]


• A hierarchical manifold learning framework for high-dimensional brain imaging data
  S. Gao, G. Mishne, D. Scheinost
  IPMI-2019, Hong Kong, June 2019.


• Learning spatially-correlated temporal dictionaries for calcium imaging
  G. Mishne and Adam S. Charles
  IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2019, Brighton, UK, May 2019.


• Hierarchical nonlinear embedding reveals brain states and performance differences during working memory tasks
  S. Gao, G. Mishne, D. Scheinost
  Conference on Cognitive Computational Neuroscience, CCN-2018, Philadelphia, PA, September 2018.


• Iterative diffusion-based anomaly detection
  G. Mishne and I. Cohen
  Proc. 42nd IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2017, New Orleans, USA, March 2017.


• Improving resolution in supervised patch-based target detection
  R. Amit, G. Mishne and R. Talmon
  Proc. 41st IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2016, Shanghai, China, March 2016.


• Multi-Channel Wafer Defect Detection Using Diffusion Maps
  G. Mishne and I. Cohen
  Proc. 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI-2014, Eilat, Israel, 3-5 December 2014.


• Multiscale Anomaly Detection Using Diffusion Maps and Saliency Score
  G. Mishne and I. Cohen
  Proc. 39th IEEE Internat. Conf. Acoust. Speech Signal Process., ICASSP-2014, Florence, Italy, May 4-9, 2014, pp. 2823-2827.

Peer-reviewed Abstracts

• Out-of-Sample Extension of Spectral Embeddings: An Optimization Perspective
  C. Holtz, T. Reja and G. Mishne
  Learning on Graphs Conference (LOG-2024)


• Hyperbolic Diffusion Embedding and Distance for Hierarchical Representation Learning
  Y-W. Lin, R.R. Coifman, G. Mishne and R. Talmon
  Beyond Euclidean Workshop: Hyperbolic and Hyperspherical Learning for Computer Vision (ECCV Workshop 2024)


• Unsupervised quantification and classification of free-moving human behavior in euthymic bipolar disorder
  Z. Zhang, C. Chou, H. Rosberg, W. Perry, J. Young, A. Minassian, M. Aoi, G. Mishne
  Computational and Systems Neuroscience (COSYNE) 2024


• SiBBlINGS: Similarity-driven Building-Block Inference using Neural-Graphs across States
  N. Mudrik, G. Mishne, A. Charles
  Computational and Systems Neuroscience (COSYNE) 2024


• Rapid fluctuations in multi-scale correlations of cortical networks encode spontaneous behavior
  H. Benisty, A. H. Moberly, S. Lohani, D. Barson, R. R. Coifman, M. Crair, G. Mishne, J. A. Cardin, M. J. Higley
  Computational and Systems Neuroscience (COSYNE) 2023


• Dissection of inter-area interactions of motor circuits
  E. Gjoni*, R. D. Sristi*, H. Liu, S. Dror, X. Lin, K. O'Neil, O. Arroyo, S. W. Wong, S. Blumenstock, B. Lim, G. Mishne#, T. Komiyama#
  Computational and Systems Neuroscience (COSYNE) 2023


• Semi-supervised quantification and interpretation of undirected human behavior
  Z. Zhang, Y. Yang, T. Sheehan, C. Chou, H. Rosberg, W. Perry, J. Young, A. Minassian, G. Mishne, M. Aoi
  Computational and Systems Neuroscience (COSYNE) 2023


• Reliable neural manifold decoding using low-distortion alignment of tangent spaces
  J. Nieuwenhuis*, D. Kohli*, A. Cloninger, G. Mishne#, D. Narain#
  Computational and Systems Neuroscience (COSYNE) 2023


• Rapid fluctuations in functional connectivity of cortical networks encode spontaneous behavior
  H. Benisty, A. H. Moberly, S. Lohani, D. Barson, R. R. Coifman, G. Mishne, J. A. Cardin and M. J. Higley
  Computational and Systems Neuroscience (COSYNE) 2022


• Multi-session Alignment for Longitudinal Calcium Imaging
  S. Yadav, R. Hattori, E-J. Hwang, A. Ramot, A. Wu, T. Komiyama and G. Mishne,
  Computational and Systems Neuroscience (COSYNE) 2021


• Embedding Disentangled Dynamics from Behavioral Videos
  C. Shi, S. Levy, M. Lavzin, J. Schiller and G. Mishne,
  Computational and Systems Neuroscience (COSYNE) 2021


• The PHATE of learning networks
  S. Gigante, A. Charles, S. Krishnaswamy and G. Mishne
  Computational and Systems Neuroscience (COSYNE) 2020


• Hierarchical Tensor Partitioning and Tiling for Learning Coupled Multiscale Neuronal Dynamics
  G. Mishne, H. Benisty, M. Lavzin, S. Levy, R. Talmon, R. Meir, J. Schiller and R. Coifman,
  Computational and Systems Neuroscience (COSYNE) 2020


• Spatially-filtered temporal dictionary learning for calcium imaging analysis
  G. Mishne, N. Cermak, J. Schiller and A. S. Charles
  SPARS 2019


• Graph-filtered temporal dictionary learning for calcium imaging analysis
  G. Mishne, B. Scott, S. Thiberge, N. Cermak, J. Schiller, C. Brody, D. W. Tank, and A. S. Charles
  Oral presentation at Annual Computational Neuroscience Meeting (CNS*2019)


• Temporal dictionary learning for calcium imaging analysis
  G. Mishne, B. Scott, S. Thiberge, N. Cermak, J. Schiller, C. Brody, D. W. Tank, and A. S. Charles
  Computational and Systems Neuroscience (COSYNE) 2019


• Local diffusion geometry for automated cellular structure extraction in calcium imaging data
  G. Mishne, R. R. Coifma, M. Lavzin and J. Schiller
  Computational and Systems Neuroscience (COSYNE) 2018