Sergey M. Plis

Orcid: 0000-0003-0040-0365

Affiliations:
  • Georgia State University, Atlanta, Georgia, USA
  • Tri-Institutional Center for Translational Research in Neuroimaging and Data Science, Atlanta, Georgia, USA
  • Mind Research Network (MRN), Albuquerque, NM, USA (former)
  • University of New Mexico, Albuquerque, Department of Computer Science, USA (former)


According to our database1, Sergey M. Plis authored at least 113 papers between 2005 and 2024.

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Bibliography

2024
HistoJS: Web-Based Analytical Tool for Advancing Multiplexed Images.
J. Open Source Softw., February, 2024

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links.
NeuroImage, January, 2024

Low-Rank Learning by Design: the Role of Network Architecture and Activation Linearity in Gradient Rank Collapse.
CoRR, 2024

2023
Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains.
Neuroinformatics, April, 2023

Brainchop: In-browser MRI volumetric segmentation and rendering.
J. Open Source Softw., March, 2023

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.
Frontiers Neuroinformatics, March, 2023

DynaLay: An Introspective Approach to Dynamic Layer Selection for Deep Networks.
CoRR, 2023

Brainchop: Next Generation Web-Based Neuroimaging Application.
CoRR, 2023

Looking deeper into interpretable deep learning in neuroimaging: a comprehensive survey.
CoRR, 2023

Learning low-dimensional dynamics from whole-brain data improves task capture.
CoRR, 2023

SalientGrads: Sparse Models for Communication Efficient and Data Aware Distributed Federated Training.
CoRR, 2023

Self-Supervised Mental Disorder Classifiers via Time Reversal.
Proceedings of the International Joint Conference on Neural Networks, 2023

GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Glacier: Glass-Box Transformer for Interpretable Dynamic Neuroimaging.
Proceedings of the IEEE International Conference on Acoustics, 2023

Causal Learning through Deliberate Undersampling.
Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022
Explicit Group Sparse Projection with Applications to Deep Learning and NMF.
Trans. Mach. Learn. Res., 2022

Deep Learning in Neuroimaging: Promises and challenges.
IEEE Signal Process. Mag., 2022

NeuroCrypt: Machine Learning Over Encrypted Distributed Neuroimaging Data.
Neuroinformatics, 2022

Federated Analysis of Neuroimaging Data: A Review of the Field.
Neuroinformatics, 2022

Decentralized Brain Age Estimation Using MRI Data.
Neuroinformatics, 2022

Through the looking glass: Deep interpretable dynamic directed connectivity in resting fMRI.
NeuroImage, 2022

Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes.
CoRR, 2022

Pipeline-Invariant Representation Learning for Neuroimaging.
CoRR, 2022

Geometrically Guided Integrated Gradients.
CoRR, 2022

Constraint-Based Causal Structure Learning from Undersampled Graphs.
CoRR, 2022

Refacing Defaced MRI with PixelCNN.
Proceedings of the International Joint Conference on Neural Networks, 2022

Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series.
Proceedings of the International Joint Conference on Neural Networks, 2022

Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder.
Proceedings of the 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2022

2021
A Correlated Noise-Assisted Decentralized Differentially Private Estimation Protocol, and its Application to fMRI Source Separation.
IEEE Trans. Signal Process., 2021

Multidataset Independent Subspace Analysis With Application to Multimodal Fusion.
IEEE Trans. Image Process., 2021

Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis.
Neuroinformatics, 2021

Single-Shot Pruning for Offline Reinforcement Learning.
CoRR, 2021

Multi network InfoMax: A pre-training method involving graph convolutional networks.
CoRR, 2021

Brain dynamics via Cumulative Auto-Regressive Self-Attention.
CoRR, 2021

Algorithm-Agnostic Explainability for Unsupervised Clustering.
CoRR, 2021

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data.
CoRR, 2021

Efficient Distributed Auto-Differentiation.
CoRR, 2021

A Classification-Based Approach to Estimate the Number of Resting Functional Magnetic Resonance Imaging Dynamic Functional Connectivity States.
Brain Connect., 2021

A Deep Learning Model for Data-Driven Discovery of Functional Connectivity.
Algorithms, 2021

Statelets: A Novel Multi-Dimensional State-Shape Representation Of Brain Functional Connectivity Dynamics.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

3-way Parallel Fusion of Spatial (sMRI/dMRI) and Spatio-temporal (fMRI) Data with Application to Schizophrenia.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer's Disease.
Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

Can recurrent models know more than we do?
Proceedings of the 9th IEEE International Conference on Healthcare Informatics, 2021

Self-Supervised Multimodal Domino: in Search of Biomarkers for Alzheimer's Disease.
Proceedings of the 9th IEEE International Conference on Healthcare Informatics, 2021

Fusing multimodal neuroimaging data with a variational autoencoder.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

Deep learning in resting-state fMRI<sup>*</sup>.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021

2020
Meta-Modal Information Flow: A Method for Capturing Multimodal Modular Disconnectivity in Schizophrenia.
IEEE Trans. Biomed. Eng., 2020

COINSTAC: Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation.
J. Open Source Softw., 2020

Taxonomy of multimodal self-supervised representation learning.
CoRR, 2020

On self-supervised multi-modal representation learning: An application to Alzheimer's disease.
CoRR, 2020

Whole MILC: Generalizing Learned Dynamics Across Tasks, Datasets, and Populations.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

Time-varying Graphs: A Method to Identify Abnormal Integration and Disconnection in Functional Brain Connectivity with Application to Schizophrenia.
Proceedings of the 20th IEEE International Conference on Bioinformatics and Bioengineering, 2020

2019
Amalgamating evidence of dynamics.
Synth., 2019

Decentralized temporal independent component analysis: Leveraging fMRI data in collaborative settings.
NeuroImage, 2019

Grouped sparse projection.
CoRR, 2019

Learnt dynamics generalizes across tasks, datasets, and populations.
CoRR, 2019

Transfer Learning of fMRI Dynamics.
CoRR, 2019

Improved Differentially Private Decentralized Source Separation for fMRI Data.
CoRR, 2019

Classification As a Criterion to Select Model Order For Dynamic Functional Connectivity States in Rest-fMRI Data.
Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019

Prediction of Progression to Alzheimer's disease with Deep InfoMax.
Proceedings of the 2019 IEEE EMBS International Conference on Biomedical & Health Informatics, 2019

2018
Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia.
NeuroImage, 2018

Decentralized Analysis of Brain Imaging Data: Voxel-Based Morphometry and Dynamic Functional Network Connectivity.
Frontiers Neuroinformatics, 2018

Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR.
CoRR, 2018

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments.
CoRR, 2018

Run, Skeleton, Run: Skeletal Model in a Physics-Based Simulation.
Proceedings of the 2018 AAAI Spring Symposia, 2018

2017
Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: Evaluation using sensorimotor tasks.
NeuroImage, 2017

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.
NeuroImage, 2017

A constraint optimization approach to causal discovery from subsampled time series data.
Int. J. Approx. Reason., 2017

COINSTAC: Decentralizing the future of brain imaging analysis.
F1000Research, 2017

Almost instant brain atlas segmentation for large-scale studies.
CoRR, 2017

Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method.
Proceedings of the 27th IEEE International Workshop on Machine Learning for Signal Processing, 2017

Cooperative learning: Decentralized data neural network.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

End-to-end learning of brain tissue segmentation from imperfect labeling.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

See without looking: joint visualization of sensitive multi-site datasets.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

Decentralized independent vector analysis.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

A deep-learning approach to translate between brain structure and functional connectivity.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016
Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling.
IEEE J. Sel. Top. Signal Process., 2016

A Tool for Interactive Data Visualization: Application to Over 10, 000 Brain Imaging and Phantom MRI Data Sets.
Frontiers Neuroinformatics, 2016

Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data.
CoRR, 2016

Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data.
CoRR, 2016

Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach.
Proceedings of the 2016 IEEE Southwest Symposium on Image Analysis and Interpretation, 2016

Causal Discovery from Subsampled Time Series Data by Constraint Optimization.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Data-weighted ensemble learning for privacy-preserving distributed learning.
Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

Privacy-preserving source separation for distributed data using independent component analysis.
Proceedings of the 2016 Annual Conference on Information Science and Systems, 2016

2015
Mesochronal Structure Learning.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Rate-Agnostic (Causal) Structure Learning.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Synthetic structural magnetic resonance image generator improves deep learning prediction of schizophrenia.
Proceedings of the 25th IEEE International Workshop on Machine Learning for Signal Processing, 2015

Deep independence network analysis of structural brain imaging: A simulation study.
Proceedings of the 25th IEEE International Workshop on Machine Learning for Signal Processing, 2015

Large scale collaboration with autonomy: Decentralized data ICA.
Proceedings of the 25th IEEE International Workshop on Machine Learning for Signal Processing, 2015

Generation of synthetic structural magnetic resonance images for deep learning pre-training.
Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, 2015

2014
A statistically motivated framework for simulation of stochastic data fusion models applied to multimodal neuroimaging.
NeuroImage, 2014

High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia.
NeuroImage, 2014

Functional and effective connectivity of stopping.
NeuroImage, 2014

Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks.
NeuroImage, 2014

Impact of autocorrelation on functional connectivity.
NeuroImage, 2014

Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation.
Frontiers Neuroinformatics, 2014

Deep learning for neuroimaging: a validation study.
Proceedings of the 2nd International Conference on Learning Representations, 2014

The tenth annual MLSP competition: Schizophrenia classification challenge.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, 2014

Multidataset independent subspace analysis extends independent vector analysis.
Proceedings of the 2014 IEEE International Conference on Image Processing, 2014

2013
Block Coordinate Descent for Sparse NMF
Proceedings of the 1st International Conference on Learning Representations, 2013

2011
Correlated Noise: How it Breaks NMF, and What to Do About it.
J. Signal Process. Syst., 2011

Directional Statistics on Permutations.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Effective connectivity analysis of fMRI and MEG data collected under identical paradigms.
Comput. Biol. Medicine, 2011

Sparseness and a reduction from Totally Nonnegative Least Squares to SVM.
Proceedings of the 2011 International Joint Conference on Neural Networks, 2011

2010
MEG and fMRI fusion for nonlinear estimation of neural and BOLD signal changes.
Frontiers Neuroinformatics, 2010

Adaptive Parallel/Serial Sampling Mechanisms for Particle Filtering in Dynamic Bayesian Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Permutations as Angular Data: Efficient Inference in Factorial Spaces.
Proceedings of the ICDM 2010, 2010

2009
Multiplicative updates For Non-Negative Kernel SVM
CoRR, 2009

Efficient Multiplicative Updates for Support Vector Machines.
Proceedings of the SIAM International Conference on Data Mining, 2009

Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

2008
Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC.
NeuroImage, 2008

2006
A generalized spatiotemporal covariance model for stationary background in analysis of MEG data.
Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

2005
Spatiotemporal Bayesian inference dipole analysis for MEG neuroimaging data.
NeuroImage, 2005


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