# Publications

## Submitted / preprints

**On a Generalization of Wasserstein Distance and the Beckmann Problem to Connection Graphs**

S. J. Robertson, D. Kohli, G. Mishne and A. Cloninger*[arXiv]***Random Walks, Conductance, and Resistance for the Connection Graph Laplacian**

A. Cloninger, G. Mishne, A. Oslandsbotn, S. J. Robertson, Z. Wan and Y. Wang*[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

**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

**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

**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