diff --git a/about/index.html b/about/index.html new file mode 100644 index 0000000..d8919fe --- /dev/null +++ b/about/index.html @@ -0,0 +1,493 @@ + + +About + + + + + + + + + + + + + + + + + + + + + +About + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Sergey Plis
Assiociate Professor of CS at GSU

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I received my Ph.D. degree in Computer Science in 2007 from The +University of New Mexico, Albuquerque, NM, USA. Currently, I am an +associate professor of CS at Georgia State University, and the +Director of Machine Learning core at the TReNDS institute. My +research interests within the fields of machine learning, AI, and data +science lie in developing novel and applying existing techniques and +approaches to analyzing large scale datasets. One of my key goals is +to take advantage of the strengths of multiple data modalities and +infer structure and patterns that are hard to obtain non-invasively +and/or that are unavailable for direct observation. Ongoing work is +focused on inferring multimodal probabilistic and causal descriptions +based on fusion of fast and slow imaging modalities. This includes +feature estimation via deep learning-based pattern recognition and +learning causal graphical models. Besides academia, I’ve led AI +research teams developing deep tech AI products including NLP and +speech processing solutions that are currently shipped to customers. +My research is supported by generous grants from NSF and NIH.

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My Experiences

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Are you Developer and recently started your own business and Already made a to ensure online presence

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Affinity Design Limited
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Gethugothemes
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Themefisher
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My Approach

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My Skills

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I Am Compatible With This Tools

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Advanced Machine Learning

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Machine learning studies algorithms that build models from data for subsequent use in prediction, …

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Sergey Plis

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Posts by Sergey Plis

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Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains

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+ Sergey Plis | + Jan 1, 2022 | + Publications + +
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Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium

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Bachelor

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Classes

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Advanced Machine Learning

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Machine learning studies algorithms that build models from data for subsequent use in prediction, inference, and decision making tasks.

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Master

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Ph.D

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representations for a spectrum of Alzheimer's phenotypes + https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Wed, 07 Sep 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Authors : Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P DeRamus, Margaux Luck, Maria Misiura, R Devon Hjelm, Sergey M Plis, Vince D Calhoun + + + Pipeline-Invariant Representation Learning for Neuroimaging + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Sat, 27 Aug 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Authors : Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, Vince Calhoun + + + Interpreting models interpreting brain dynamics + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Thu, 21 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Authors : Md Rahman, Usman Mahmood, Noah Lewis, Harshvardhan Gazula, Alex Fedorov, Zening Fu, Vince D Calhoun, Sergey M Plis + + + Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Mon, 11 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Authors : Xinhui Li, Eloy Geenjaar, Zening Fu, Sergey Plis, Vince Calhoun + + + Statelets: Capturing recurrent transient variations in dynamic functional network connectivity + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Wed, 01 Jun 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Authors : Md Abdur Rahaman, Eswar Damaraju, Debbrata K Saha, Sergey M Plis, Vince D Calhoun + + + Constraint-Based Causal Structure Learning from Undersampled Graphs + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Wed, 18 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Authors : Mohammadsajad Abavisani, David Danks, Sergey Plis Publication date : 2022/5/18 + + + Privacy‐preserving quality control of neuroimaging datasets in federated environments + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Sun, 01 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Authors : Debbrata K Saha, Vince D Calhoun, Yuhui Du, Zening Fu, Soo Min Kwon, Anand D Sarwate, Sandeep R Panta, Sergey M Plis + + + Decentralized Brain Age Estimation using MRI Data + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Tue, 05 Apr 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Authors : Sunitha Basodi, Rajikha Raja, Bhaskar Ray, Harshvardhan Gazula, Anand D Sarwate, Sergey Plis, Jingyu Liu, Eric Verner, Vince D Calhoun + + + Geometrically Guided Saliency Maps + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Sat, 05 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Authors : Md Mahfuzur Rahman, Noah Lewis, Sergey Plis Publication date : 2014/3/1 + + + Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Tue, 01 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Authors : Shile Qi, Rogers F Silva, Daoqiang Zhang, Sergey M Plis, Robyn Miller, Victor M Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D Calhoun + + + Deep learning in neuroimaging: Promises and challenges + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Thu, 24 Feb 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Authors : Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M Plis, Yu-Ping Wang, Jing Sui, Vince D Calhoun + + + Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium + + + Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Authors : Haleh Falakshahi, Hooman Rokham, Zening Fu, Armin Iraji, Daniel H Mathalon, Judith M Ford, Bryon A Mueller, Adrian Preda, Theo GM van Erp, Jessica A Turner, Sergey Plis, Vince D Calhoun + + + Single-shot pruning for offline reinforcement learning + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Fri, 31 Dec 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Authors : Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Doina Precup + + + Federated analysis of neuroimaging data: A review of the field + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Mon, 22 Nov 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + Tracking whole-brain connectivity dynamics in the resting state + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Sat, 01 Mar 2014 05:00:00 +0000 + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + diff --git a/categories/publications/page/1/index.html b/categories/publications/page/1/index.html new file mode 100644 index 0000000..cf22711 --- /dev/null +++ b/categories/publications/page/1/index.html @@ -0,0 +1,10 @@ + + + + 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Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains

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+ Sergey Plis | + Jan 1, 2022 | + Publications + +
+

Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium

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    TablesAreCool
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    Advanced Machine Learning

    +
    + | + Apr 23, 2021| + Classes + +
    + + Advanced Machine Learning + + + +
    +

    Machine learning studies algorithms that build models from data for +subsequent use in prediction, inference, and decision making +tasks. Although an active field for the last 60 years, the current +demand as well as trust in machine learning exploded as increasingly +more data become available and the problems needed to be addressed +become literally impossible to program directly. In this advanced +course we will cover essential algorithms, concepts, and principles of +machine learning. Along with the traditional exposition we will learn +how these principles are currently being revisited thanks to the +recent discoveries in the field.

    +

    1. Introduction

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Introductions 7:51
      2. +
    2. Why Machine +Learning +12:00
      3. +
    3. What is Machine Learning 18:57
      4. +
    4. History of Machine +Learning 17:33
      5. +
    5. Reinforcement Learning 10:32
      6. +
    6. Course Overview 19:26
      7. +
    7. The +Project +20:03
      8. +
    +

    2. Foundations of learning

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Formalizing the Problem of +Learning 24:19
      2. +
    2. Inductive Bias 12:03
      3. +
    3. Can We Bound the Probability of Error? +25:56
      4. +
    +

    3. PAC learnability

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Main Definitions from Lecture +2 +13:52
      2. +
    2. Agnostic PAC +Learning +53:35
      3. +
    3. Learning via Uniform +Convergence +10:15
      4. +
    +

    4. Linear algebra and Optimization (recap)

    +
      +
    1. 3Blue1Brown Playlist
      2. +
    +

    5. Linear learning models

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Linear Decision +Boundary 34:10
      2. +
    2. Perceptron +37:10
      3. +
    3. Perceptron +Extensions +14:09
      4. +
    4. Linear Classifier for Linearly non Separable +Classes +8:59
      5. +
    +

    6. Principal Component Analysis

    +

    Lecture Slides

    +

    Youtube lectures

    +
      +
    1. Linear +Regression +39:24
      2. +
    2. Linear Algebra Micro +Refresher +2:04
      3. +
    3. Spectral +Theorem +25:54
      4. +
    4. Principal Component +Analysis +22:29
      5. +
    5. Demonstration 17:38
      6. +
    +

    7. Curse of Dimensionality

    +

    Lecture Slides

    +

    Youtube lectures

    +
      +
    1. Curse of Dimensionality 1:16:27
      2. +
    +

    8. Bayesian Decision Theory

    +

    Lecture Slides

    +

    Youtube lectures

    +
      +
    1. Bayesian Decision Theory 56:47
      2. +
    +

    9. Parameter estimation: MLE

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Independence +12:07
      2. +
    2. Maximum Likelihood Estimation 50:35
      3. +
    3. MLE as KL-divergence +minimization 21:41
      4. +
    +

    10. Parameter estimation: MAP & Naïve Bayes

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. MAP +Estimation +56:00
      2. +
    2. The Naïve Bayes Classifier 37:09
      3. +
    +

    11. Logistic Regression

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. NB to +LR +19:49
      2. +
    2. Defining Logistic +Regression +27:42
      3. +
    3. Solving Logistic Regression 23:35
      4. +
    +

    12. Kernel Density Estimation

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Non-parametric Density Estimation 1:13:33
      2. +
    +

    13. Support Vector Machines

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Max Margin +Classifier +35:53
      2. +
    2. Lagrange +Multipliers +32:45
      3. +
    3. Dual Formulation of Linear +SVM +10:34
      4. +
    4. Kernel Trick and Soft Margin 27:28
      5. +
    +

    14. Matrix Factorization

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Matrix Factorization 1:24:22
      2. +
    +

    15. Stochastic Gradient Descent

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Stochastic Gradient Descent 1:06:57
      2. +
    +

    16. k-means Clustering

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Clustering +6:05
      2. +
    2. Gaussian Mixture +Models +16:34
      3. +
    3. MLE +recap +4:20
      4. +
    4. Hard k-means +Clustering +30:27
      5. +
    5. Soft k-means Clustering 7:18
      6. +
    +

    17. Expectation Maximization

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Do we even need EM for +GMM? +14:39
      2. +
    2. A “hacky” GMM +estimation +15:17
      3. +
    3. MLE via +EM 38:28
      4. +
    +

    18. Automatic Differentiation

    +

    Lecture Slides

    +

    Youtube Lectures

    +
      +
    1. Introduction +25:10
      2. +
    2. Forward Mode +AD +26:46
      3. +
    3. A minute of +Backprop +2:26
      4. +
    4. Reverse mode +AD +17:26
      5. +
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    19. Nonlinear Embedding Approaches

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    20. Model Comparison I

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    1. Building +Blocks +39:22
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    24. Word Embedding

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    1. Introduction +10:35
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    Causal Learning through Deliberate Undersampling

    +
    + Sergey Plis | + Feb 20, 2023| + Publications + +
    + + Causal Learning through Deliberate Undersampling + + + +
    +

    Authors : Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis

    +

    Publication date : 2023/2/20

    +

    Journal : Proceedings of Machine Learning Research

    +

    Volume : 13

    +

    Issue :

    +

    Pages :

    +

    Publisher : Cambridge MA: JMLR

    +

    Description

    +

    Domain scientists interested in causal mechanisms are usually limited by the frequency at which they can collect the measurements of social, physical, or biological systems. +A common and plausible assumption is that higher measurement frequencies are the only way to gain more informative data about the underlying dynamical causal structure. +This assumption is a strong driver for designing new, faster instruments, but such instruments might not be feasible or even possible. +In this paper, we show that this assumption is incorrect: there are situations in which we can gain additional information about the causal structure by measuring more than our current instruments. +We present an algorithm that uses graphs at multiple measurement timescales to infer underlying causal structure, and show that inclusion of structures at slower timescales can nonetheless reduce the size of the equivalence class of possible causal structures. +We provide simulation data about the probability of cases in which deliberate undersampling yields a gain, as well as the size of this gain.

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    Constraint-Based Causal Structure Learning from Undersampled Graphs

    +
    + Sergey Plis | + May 18, 2022| + Publications + +
    + + Constraint-Based Causal Structure Learning from Undersampled Graphs + + + +
    +

    Authors : Mohammadsajad Abavisani, David Danks, Sergey Plis

    +

    Publication date : 2022/5/18

    +

    Journal : arXiv preprint arXiv:2205.09235

    +

    Description

    +

    Graphical structures estimated by causal learning algorithms from time series data can provide highly misleading causal information if the causal timescale of the generating process fails to match the measurement timescale of the data. Although this problem has been recently recognized, practitioners have limited resources to respond to it, and so must continue using models that they know are likely misleading. Existing methods either (a) require that the difference between causal and measurement timescales is known; or (b) can handle only very small number of random variables when the timescale difference is unknown; or (c) apply to only pairs of variables, though with fewer assumptions about prior knowledge; or (d) return impractically too many solutions. This paper addresses all four challenges. We combine constraint programming with both theoretical insights into the problem structure and prior information about admissible causal interactions. The resulting system provides a practical approach that scales to significantly larger sets (>100) of random variables, does not require precise knowledge of the timescale difference, supports edge misidentification and parametric connection strengths, and can provide the optimum choice among many possible solutions. The cumulative impact of these improvements is gain of multiple orders of magnitude in speed and informativeness.

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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/posts/decentralized-brain-age-estimation-using-mri-data/index.html b/posts/decentralized-brain-age-estimation-using-mri-data/index.html new file mode 100644 index 0000000..dfdcf42 --- /dev/null +++ b/posts/decentralized-brain-age-estimation-using-mri-data/index.html @@ -0,0 +1,519 @@ + + +Decentralized Brain Age Estimation using MRI Data + + + + + + + + + + + + + + + + + + + + + +Decentralized Brain Age Estimation using MRI Data + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Decentralized Brain Age Estimation using MRI Data

    +
    + Sergey Plis | + Apr 5, 2022| + Publications + +
    + + Decentralized Brain Age Estimation using MRI Data + + + +
    +

    Authors : Sunitha Basodi, Rajikha Raja, Bhaskar Ray, Harshvardhan Gazula, Anand D Sarwate, Sergey Plis, Jingyu Liu, Eric Verner, Vince D Calhoun

    +

    Publication date : 2022/4/5

    +

    Journal : Neuroinformatics

    +

    Pages : 1-10

    +

    Publisher : Springer US

    +

    Description

    +

    Recent studies have demonstrated that neuroimaging data can be used to estimate biological brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized method for biological brain age estimation using support vector regression models and evaluate it on three different feature sets, including both volumetric and voxelwise structural MRI data as well as resting functional MRI data. The results demonstrate that our decentralized brain age …

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    Deep learning in neuroimaging: Promises and challenges

    +
    + Sergey Plis | + Feb 24, 2022| + Publications + +
    + + Deep learning in neuroimaging: Promises and challenges + + + +
    +

    Authors : Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M Plis, Yu-Ping Wang, Jing Sui, Vince D Calhoun

    +

    Publication date : 2022/2/24

    +

    Journal : IEEE Signal Processing Magazine

    +

    Volume : 39

    +

    Issue : 2

    +

    Pages : 87-98

    +

    Publisher : IEEE

    +

    Description

    +

    Deep learning (DL) has been extremely successful when applied to the analysis of natural images. By contrast, analyzing neuroimaging data presents some unique challenges, including higher dimensionality, smaller sample sizes, multiple heterogeneous modalities, and a limited ground truth. In this article, we discuss DL methods in the context of four diverse and important categories in the neuroimaging field: classification/prediction, dynamic activity/connectivity, multimodal fusion, and interpretation/visualization. We highlight recent progress in each of these categories, discuss the benefits of combining data characteristics and model architectures, and derive guidelines for the use of DL in neuroimaging data. For each category, we also assess promising applications and major challenges to overcome. Finally, we discuss future directions of neuroimaging DL for clinical applications, a topic of great interest …

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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/index.html b/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/index.html new file mode 100644 index 0000000..3636123 --- /dev/null +++ b/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/index.html @@ -0,0 +1,516 @@ + + +Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains + + + + + + + + + + + + + + + + + + + + + +Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains

    +
    + Sergey Plis | + Jan 1, 2022| + Publications + +
    + + Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains + + + +
    +

    Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium

    +

    Publication date : 2022/1/1

    +

    Journal : bioRxiv

    +

    Publisher : Cold Spring Harbor Laboratory

    +

    Description

    +

    With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

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    Federated analysis of neuroimaging data: A review of the field

    +
    + Sergey Plis | + Nov 22, 2021| + Publications + +
    + + Federated analysis of neuroimaging data: A review of the field + + + +
    +

    Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun

    +

    Publication date : 2021/11/22

    +

    Source : Neuroinformatics

    +

    Pages : 1-14

    +

    Publisher : Springer US

    +

    Description

    +

    The field of neuroimaging has embraced sharing data to collaboratively advance our understanding of the brain. However, data sharing, especially across sites with large amounts of protected health information (PHI), can be cumbersome and time intensive. Recently, there has been a greater push towards collaborative frameworks that enable large-scale federated analysis of neuroimaging data without the data having to leave its original location. However, there still remains a need for a standardized federated approach that not only allows for data sharing adhering to the FAIR (Findability, Accessibility, Interoperability, Reusability) data principles, but also streamlines analyses and communication while maintaining subject privacy. In this paper, we review a non-exhaustive list of neuroimaging analytic tools and frameworks currently in use. We then provide an update on our federated neuroimaging analysis …

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    Geometrically Guided Saliency Maps

    +
    + Sergey Plis | + Mar 5, 2022| + Publications + +
    + + Geometrically Guided Saliency Maps + + + +
    +

    Authors : Md Mahfuzur Rahman, Noah Lewis, Sergey Plis

    +

    Publication date : 2014/3/1

    +

    Conference : ICLR 2022 Workshop on PAIR {\textasciicircum

    +

    Description

    +

    Interpretability methods for deep neural networks mainly focus on modifying the rules of automatic differentiation or perturbing the input and observing the score drop to determine the most relevant features. Among them, gradient-based attribution methods, such as saliency maps, are arguably the most popular. Still, the produced saliency maps may often lack intelligibility. We address this problem based on recent discoveries in geometric properties of deep neural networks’ loss landscape that reveal the existence of a multiplicity of local minima in the vicinity of a trained model’s loss surface. We introduce two methods that leverage the geometry of the loss landscape to improve interpretability: 1)" Geometrically Guided Integrated Gradients", applying gradient ascent to each interpolation point of the linear path as a guide. 2)" Geometric Ensemble Gradients", generating ensemble saliency maps by sampling proximal iso-loss models. Compared to vanilla and integrated gradients, these methods significantly improve saliency maps in quantitative and visual terms. We verify our findings on MNIST and Imagenet datasets across convolutional, ResNet, and Inception V3 architectures.

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    Advanced Machine Learning

    +
    + | + Apr 23, 2021 | + Classes + +
    +

    Machine learning studies algorithms that build models from data for subsequent use in prediction, inference, and decision making tasks.

    + read more +
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/posts/index.xml b/posts/index.xml new file mode 100644 index 0000000..48190ae --- /dev/null +++ b/posts/index.xml @@ -0,0 +1,145 @@ + + + + Posts on neuroneural | Sergey Plis, Ph.D. + https://neuroneural.github.io/posts/ + Recent content in Posts on neuroneural | Sergey Plis, Ph.D. + Hugo -- gohugo.io + en-us + Mon, 20 Feb 2023 05:00:00 +0000 + + + Advanced Machine Learning + https://neuroneural.github.io/posts/advancedml/ + Fri, 23 Apr 2021 17:20:31 -0400 + https://neuroneural.github.io/posts/advancedml/ + Machine learning studies algorithms that build models from data for subsequent use in prediction, inference, and decision making tasks. + + + Causal Learning through Deliberate Undersampling + https://neuroneural.github.io/posts/causal-learning-through-deliberate-undersampling/ + Mon, 20 Feb 2023 05:00:00 +0000 + https://neuroneural.github.io/posts/causal-learning-through-deliberate-undersampling/ + Authors : Kseniya Solovyeva, David Danks, Mohammadsajad Abavisani, Sergey Plis + + + Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes + https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Wed, 07 Sep 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Authors : Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P DeRamus, Margaux Luck, Maria Misiura, R Devon Hjelm, Sergey M Plis, Vince D Calhoun + + + Pipeline-Invariant Representation Learning for Neuroimaging + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Sat, 27 Aug 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Authors : Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, Vince Calhoun + + + Interpreting models interpreting brain dynamics + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Thu, 21 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Authors : Md Rahman, Usman Mahmood, Noah Lewis, Harshvardhan Gazula, Alex Fedorov, Zening Fu, Vince D Calhoun, Sergey M Plis + + + Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Mon, 11 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Authors : Xinhui Li, Eloy Geenjaar, Zening Fu, Sergey Plis, Vince Calhoun + + + Statelets: Capturing recurrent transient variations in dynamic functional network connectivity + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Wed, 01 Jun 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Authors : Md Abdur Rahaman, Eswar Damaraju, Debbrata K Saha, Sergey M Plis, Vince D Calhoun + + + Constraint-Based Causal Structure Learning from Undersampled Graphs + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Wed, 18 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Authors : Mohammadsajad Abavisani, David Danks, Sergey Plis Publication date : 2022/5/18 + + + Privacy‐preserving quality control of neuroimaging datasets in federated environments + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Sun, 01 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Authors : Debbrata K Saha, Vince D Calhoun, Yuhui Du, Zening Fu, Soo Min Kwon, Anand D Sarwate, Sandeep R Panta, Sergey M Plis + + + Decentralized Brain Age Estimation using MRI Data + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Tue, 05 Apr 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Authors : Sunitha Basodi, Rajikha Raja, Bhaskar Ray, Harshvardhan Gazula, Anand D Sarwate, Sergey Plis, Jingyu Liu, Eric Verner, Vince D Calhoun + + + Geometrically Guided Saliency Maps + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Sat, 05 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Authors : Md Mahfuzur Rahman, Noah Lewis, Sergey Plis Publication date : 2014/3/1 + + + Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Tue, 01 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Authors : Shile Qi, Rogers F Silva, Daoqiang Zhang, Sergey M Plis, Robyn Miller, Victor M Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D Calhoun + + + Deep learning in neuroimaging: Promises and challenges + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Thu, 24 Feb 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Authors : Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M Plis, Yu-Ping Wang, Jing Sui, Vince D Calhoun + + + Introduction to Deep Learning + https://neuroneural.github.io/posts/introduction_to_dl/ + Mon, 14 Feb 2022 00:00:00 -0400 + https://neuroneural.github.io/posts/introduction_to_dl/ + Introduction to Deep Learning Welcome to the introduction to deep learning course! + + + Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium + + + Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Authors : Haleh Falakshahi, Hooman Rokham, Zening Fu, Armin Iraji, Daniel H Mathalon, Judith M Ford, Bryon A Mueller, Adrian Preda, Theo GM van Erp, Jessica A Turner, Sergey Plis, Vince D Calhoun + + + Single-shot pruning for offline reinforcement learning + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Fri, 31 Dec 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Authors : Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Doina Precup + + + Federated analysis of neuroimaging data: A review of the field + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Mon, 22 Nov 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + Tracking whole-brain connectivity dynamics in the resting state + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Sat, 01 Mar 2014 05:00:00 +0000 + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + diff --git a/posts/interpreting-models-interpreting-brain-dynamics/index.html b/posts/interpreting-models-interpreting-brain-dynamics/index.html new file mode 100644 index 0000000..e7b8af5 --- /dev/null +++ b/posts/interpreting-models-interpreting-brain-dynamics/index.html @@ -0,0 +1,521 @@ + + +Interpreting models interpreting brain dynamics + + + + + + + + + + + + + + + + + + + + + +Interpreting models interpreting brain dynamics + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Interpreting models interpreting brain dynamics

    +
    + Sergey Plis | + Jul 21, 2022| + Publications + +
    + + Interpreting models interpreting brain dynamics + + + +
    +

    Authors : Md Rahman, Usman Mahmood, Noah Lewis, Harshvardhan Gazula, Alex Fedorov, Zening Fu, Vince D Calhoun, Sergey M Plis

    +

    Publication date : 2022/7/21

    +

    Journal : Scientific reports

    +

    Volume : 12

    +

    Issue : 1

    +

    Pages : 1-15

    +

    Publisher : Nature Publishing Group

    +

    Description

    +

    Brain dynamics are highly complex and yet hold the key to understanding brain function and dysfunction. The dynamics captured by resting-state functional magnetic resonance imaging data are noisy, high-dimensional, and not readily interpretable. The typical approach of reducing this data to low-dimensional features and focusing on the most predictive features comes with strong assumptions and can miss essential aspects of the underlying dynamics. In contrast, introspection of discriminatively trained deep learning models may uncover disorder-relevant elements of the signal at the level of individual time points and spatial locations. Yet, the difficulty of reliable training on high-dimensional low sample size datasets and the unclear relevance of the resulting predictive markers prevent the widespread use of deep learning in functional neuroimaging. In this work, we introduce a deep learning framework to learn …

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    Introduction to Deep Learning

    +
    + | + Feb 14, 2022| + Classes + +
    + + + +
    +

    Introduction to Deep Learning

    +

    Welcome to the introduction to deep learning course! +​ +This course is designed to provide you with a solid foundation in the +fundamentals of deep learning. Throughout this course, you will learn +about the basic building blocks of deep learning, including basics of +machine learning, convolutional neural networks, and natural language +processing. You will also gain an understanding of how deep learning +algorithms are used to solve a variety of real-world problems, such as +image classification, natural language processing and a few advance +approaches such as GANs. +​

    +

    By the end of the course, you will have a solid understanding of the core concepts and techniques used in deep learning, as well as hands-on experience building and training your own deep learning models using popular frameworks such as PyTorch and Catalyst +​

    +

    Dr. Sergey Plis is the instructor for this course, bringing his +expertise of an active researcher in the fields of neuroscience and +computer science. He has extensive experience applying machine +learning algorithms to the analysis of brain imaging data. He is also +an experienced educator, having taught numerous courses in data +science, machine learning, and deep learning at the graduate and +undergraduate levels. +​

    +

    The hands-on part of the course has been developed by Mrinal Mathur, a +seasoned machine learning engineer with experience building and +deploying machine learning models for a variety of industries. Mrinal +has a deep understanding of the underlying mathematical and +statistical concepts that power deep learning algorithms, and he has a +passion for teaching others about the exciting possibilities of this +field. +​

    +

    Together, we have designed a comprehensive and engaging course that +will provide you with the knowledge and skills you need to succeed in +the exciting field of deep learning. +​

    +

    Introduction to Deep Learning

    +

    1. Introduction

    +

    Lecture Slides

    +
      +
    1. Introduction to Collab
      2. +
    2. Pandas (optional)
      3. +
    +

    Lecture Slides

    +
      +
    1. Numpy
      2. +
    +

    Machine Learning

    +

    2. Foundations of Machine Learning

    +
      +
    1. +

      Calculus and Optimization

      +

      Lecture Slides

      +
      2. +
    2. +

      Linear +Regression/Classification

      +

      Lecture Slides

      +
      3. +
    3. +

      Perceptron

      +

      Lecture Slides

      +
      4. +
    +

    3. Automatic Differentitation

    +

    Lecture Slides

    +

    Colab Notebooks

    + +

    4. Practice for Automatic Differentiation

    +

    Lecture Slides

    +

    5. Pytorch

    +

    Colab Notebooks

    + +

    6. Model Comparision

    +

    Lecture slides

    +

    Colab Notebook

    + +

    Computer Vision

    +

    7. Computer Vision and Image Processing

    +

    Lecture Slides

    +

    Colab Notebook

    + +

    8. Convolution Neural Network

    +

    Lecture Slides

    +

    Colab Notebook:

    + +

    9. Image Classification

    +

    Lecture Slides

    +

    Colab Notebook

    + +

    10. Skip Connections and ResNets

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    Colab Notebook

    + +

    11. Segmentation

    +

    Lecture Slides

    +

    Colab Notebook

    + +

    12. Auto-Encoders

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    Lecture Slides

    +

    Colab Notebooks:

    + +

    13. Generative Adversarial Nets

    +

    Lecture Slides

    +

    Colab Notebook:

    + +

    14. Regularization

    +

    Lecture Slides

    +

    Natural Language Processing

    +

    15. Introduction to NLP

    +

    Lecture Slides

    +

    Colab Notebooks:

    + +

    16. Recurrent Neural Networks

    +

    Colab Notebooks:

    + +

    17. LSTM and GRU

    +

    Lecture Slides

    +

    Colab Notebook:

    + +

    18. Seq2Seq2

    +

    Lecture Slides

    +

    Colab Notebooks:

    + +

    19. Attention is all you need!

    +

    Lecture Slides

    +

    Colab Notebooks:

    + +

    20. Transformers

    +

    Lecture Slides

    +

    Colab Notebooks:

    + +

    Advance Topics (To be added)

    +
      +
    1. [Graph Neural Networks]
      2. +
    2. [Reinforcement Learning]
      3. +
    3. [Meta Learning]
      4. +
    4. [Adversarial Learning]
      5. +
    5. [Transfer Learning]
      6. +
    6. [Self Supervised Learning]
      7. +
    7. [Few Shot Learning]
      8. +
    8. [Active Learning]
      9. +
    9. [Multi Task Learning]
      10. +
    10. [Multi Modal Learning]
      11. +
    11. [Domain Adaptation]
      12. +
    12. [Continual Learning]
      13. +
    13. [Causal Learning]
      14. +
    + +
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    Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder

    +
    + Sergey Plis | + Jul 11, 2022| + Publications + +
    + + Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder + + + +
    +

    Authors : Xinhui Li, Eloy Geenjaar, Zening Fu, Sergey Plis, Vince Calhoun

    +

    Publication date : 2022/7/11

    +

    Conference : 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

    +

    Pages : 1477-1480

    +

    Publisher : IEEE

    +

    Description

    +

    Mental disorders such as schizophrenia have been challenging to characterize due in part to their heterogeneous presentation in individuals. Most studies have focused on identifying groups differences and have typically ignored the heterogeneous patterns within groups. Here we propose a novel approach based on a variational autoencoder (VAE) to interpolate static functional network connectivity (sFNC) across individuals, with group-specific patterns between schizophrenia patients and controls captured simultaneously. We then visualize the original sFNC in a 2D grid according to the samples in the VAE latent space. We observe a high correspondence between the generated and the original sFNC. The proposed framework facilitates data visualization and can potentially be applied to predict the stage that a subject falls within a disorder continuum as well as characterize individual heterogeneity within and …

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    Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains

    +
    + Sergey Plis | + Jan 1, 2022 | + Publications + +
    +

    Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium

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    Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data

    +
    + Sergey Plis | + Jan 1, 2022| + Publications + +
    + + Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data + + + +
    +

    Authors : Haleh Falakshahi, Hooman Rokham, Zening Fu, Armin Iraji, Daniel H Mathalon, Judith M Ford, Bryon A Mueller, Adrian Preda, Theo GM van Erp, Jessica A Turner, Sergey Plis, Vince D Calhoun

    +

    Publication date : 2022/1

    +

    Journal : Network Neuroscience

    +

    Pages : 1-45

    +

    Description

    +

    Graph-theoretical methods have been widely used to study human brain networks in psychiatric disorders. However, the focus has primarily been on global graphic metrics with little attention to the information contained in paths connecting brain regions. Details of disruption of these paths may be highly informative for understanding disease mechanisms. To detect the absence or addition of multistep paths in the patient group, we provide an algorithm estimating edges that contribute to these paths with reference to the control group. We next examine where pairs of nodes were connected through paths in both groups by using a covariance decomposition method. We apply our method to study resting-state fMRI data in schizophrenia versus controls. Results show several disconnectors in schizophrenia within and between functional domains, particularly within the default mode and cognitive control networks …

    +

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    Pipeline-Invariant Representation Learning for Neuroimaging

    +
    + Sergey Plis | + Aug 27, 2022| + Publications + +
    + + Pipeline-Invariant Representation Learning for Neuroimaging + + + +
    +

    Authors : Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, Vince Calhoun

    +

    Publication date : 2022/8/27

    +

    Journal : arXiv preprint arXiv:2208.12909

    +

    Description

    +

    Deep learning has been widely applied in neuroimaging, including to predicting brain-phenotype relationships from magnetic resonance imaging (MRI) volumes. MRI data usually requires extensive preprocessing before it is ready for modeling, even via deep learning, in part due to its high dimensionality and heterogeneity. A growing array of MRI preprocessing pipelines have been developed each with its own strengths and limitations. Recent studies have shown that pipeline-related variation may lead to different scientific findings, even when using the identical data. Meanwhile, the machine learning community has emphasized the importance of shifting from model-centric to data-centric approaches given that data quality plays an essential role in deep learning applications. Motivated by this idea, we first evaluate how preprocessing pipeline selection can impact the downstream performance of a supervised learning model. We next propose two pipeline-invariant representation learning methodologies, MPSL and PXL, to improve consistency in classification performance and to capture similar neural network representations between pipeline pairs. Using 2000 human subjects from the UK Biobank dataset, we demonstrate that both models present unique advantages, in particular that MPSL can be used to improve out-of-sample generalization to new pipelines, while PXL can be used to improve predictive performance consistency and representational similarity within a closed pipeline set. These results suggest that our proposed models can be applied to overcome pipeline-related biases and to improve reproducibility in neuroimaging …

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    Privacy‐preserving quality control of neuroimaging datasets in federated environments

    +
    + Sergey Plis | + May 1, 2022| + Publications + +
    + + Privacy‐preserving quality control of neuroimaging datasets in federated environments + + + +
    +

    Authors : Debbrata K Saha, Vince D Calhoun, Yuhui Du, Zening Fu, Soo Min Kwon, Anand D Sarwate, Sandeep R Panta, Sergey M Plis

    +

    Publication date : 2022/5/1

    +

    Journal : Human Brain Mapping

    +

    Volume : 43

    +

    Issue : 7

    +

    Pages : 2289-2310

    +

    Publisher : John Wiley & Sons, Inc.

    +

    Description

    +

    Privacy concerns for rare disease data, institutional or IRB policies, access to local computational or storage resources or download capabilities are among the reasons that may preclude analyses that pool data to a single site. A growing number of multisite projects and consortia were formed to function in the federated environment to conduct productive research under constraints of this kind. In this scenario, a quality control tool that visualizes decentralized data in its entirety via global aggregation of local computations is especially important, as it would allow the screening of samples that cannot be jointly evaluated otherwise. To solve this issue, we present two algorithms: decentralized data stochastic neighbor embedding, dSNE, and its differentially private counterpart, DP‐dSNE. We leverage publicly available datasets to simultaneously map data samples located at different sites according to their similarities …

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    Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer’s phenotypes

    +
    + Sergey Plis | + Sep 7, 2022| + Publications + +
    + + Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer’s phenotypes + + + +
    +

    Authors : Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P DeRamus, Margaux Luck, Maria Misiura, R Devon Hjelm, Sergey M Plis, Vince D Calhoun

    +

    Publication date : 2022/9/7

    +

    Journal : arXiv preprint arXiv:2209.02876

    +

    Description

    +

    Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameterized models.However, a single modality provides only a limited view of the highly complex brain. Critically, supervised models in clinical settings lack accurate diagnostic labels for training. Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings. This work presents a novel multi-scale coordinated framework for learning multiple representations from multimodal neuroimaging data. We propose a general taxonomy of informative inductive biases to capture unique and joint information in multimodal self-supervised fusion. The taxonomy forms a family of decoder-free models with reduced computational complexity and a propensity to capture multi-scale relationships between local and global representations of the multimodal inputs. We conduct a comprehensive evaluation of the taxonomy using functional and structural magnetic resonance imaging (MRI) data across a spectrum of Alzheimer’s disease phenotypes and show that self-supervised models reveal disorder-relevant brain regions and multimodal links without access to the labels during pre-training. The proposed multimodal self-supervised learning yields representations with improved classification performance for both modalities. The concomitant rich and flexible unsupervised deep learning framework captures complex multimodal relationships and provides predictive …

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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/posts/single-shot-pruning-for-offline-reinforcement-learning/index.html b/posts/single-shot-pruning-for-offline-reinforcement-learning/index.html new file mode 100644 index 0000000..aa01437 --- /dev/null +++ b/posts/single-shot-pruning-for-offline-reinforcement-learning/index.html @@ -0,0 +1,511 @@ + + +Single-shot pruning for offline reinforcement learning + + + + + + + + + + + + + + + + + + + + + +Single-shot pruning for offline reinforcement learning + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Single-shot pruning for offline reinforcement learning

    +
    + Sergey Plis | + Dec 31, 2021| + Publications + +
    + + Single-shot pruning for offline reinforcement learning + + + +
    +

    Authors : Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Doina Precup

    +

    Publication date : 2021/12/31

    +

    Journal : arXiv preprint arXiv:2112.15579

    +

    Description

    +

    Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. Large neural networks employed in the framework are traditionally associated with better generalization capabilities, but their increased size entails the drawbacks of extensive training duration, substantial hardware resources, and longer inference times. One way to tackle this problem is to prune neural networks leaving only the necessary parameters. State-of-the-art concurrent pruning techniques for imposing sparsity perform demonstrably well in applications where data distributions are fixed. However, they have not yet been substantially explored in the context of RL. We close the gap between RL and single-shot pruning techniques and present a general pruning approach to the Offline RL. We leverage a fixed dataset to prune neural networks before the start of RL training. We then run experiments varying the network sparsity level and evaluating the validity of pruning at initialization techniques in continuous control tasks. Our results show that with 95% of the network weights pruned, Offline-RL algorithms can still retain performance in the majority of our experiments. To the best of our knowledge, no prior work utilizing pruning in RL retained performance at such high levels of sparsity. Moreover, pruning at initialization techniques can be easily integrated into any existing Offline-RL algorithms without changing the learning objective.

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    Statelets: Capturing recurrent transient variations in dynamic functional network connectivity

    +
    + Sergey Plis | + Jun 1, 2022| + Publications + +
    + + Statelets: Capturing recurrent transient variations in dynamic functional network connectivity + + + +
    +

    Authors : Md Abdur Rahaman, Eswar Damaraju, Debbrata K Saha, Sergey M Plis, Vince D Calhoun

    +

    Publication date : 2022/6/1

    +

    Journal : Human Brain Mapping

    +

    Volume : 43

    +

    Issue : 8

    +

    Pages : 2503-2518

    +

    Publisher : John Wiley & Sons, Inc.

    +

    Description

    +

    Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short‐lived in practice. Thus, SWC is neither designed to capture transient dynamic changes nor heterogeneity across subjects/time. We propose a state‐space time series summarization framework called “statelets” to address these shortcomings. It models functional connectivity dynamics at fine‐grained timescales, adapting time series motifs to changes in connectivity strength, and constructs a concise yet informative representation of the original data that conveys easily …

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    Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

    +
    + Sergey Plis | + Mar 1, 2022| + Publications + +
    + + Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data + + + +
    +

    Authors : Shile Qi, Rogers F Silva, Daoqiang Zhang, Sergey M Plis, Robyn Miller, Victor M Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D Calhoun

    +

    Publication date : 2022/3/1

    +

    Journal : Human brain mapping

    +

    Volume : 43

    +

    Issue : 4

    +

    Pages : 1280-1294

    +

    Publisher : John Wiley & Sons, Inc.

    +

    Description

    +

    Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three‐way parallel group independent component analysis (pGICA) fusion method that incorporates the first‐level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject‐wise functional variability and then …

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    Tracking whole-brain connectivity dynamics in the resting state

    +
    + Sergey Plis | + Mar 1, 2014| + Publications + +
    + + Tracking whole-brain connectivity dynamics in the resting state + + + +
    +

    Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun

    +

    Publication date : 2014/3/1

    +

    Journal : Cerebral cortex

    +

    Volume : 24

    +

    Issue : 3

    +

    Pages : 663-676

    +

    Publisher : Oxford University Press

    +

    Description

    +

    Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal …

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https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Wed, 07 Sep 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Authors : Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P DeRamus, Margaux Luck, Maria Misiura, R Devon Hjelm, Sergey M Plis, Vince D Calhoun + + + Pipeline-Invariant Representation Learning for Neuroimaging + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Sat, 27 Aug 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Authors : Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, Vince Calhoun + + + Interpreting models interpreting brain dynamics + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Thu, 21 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Authors : Md Rahman, Usman Mahmood, Noah Lewis, Harshvardhan Gazula, Alex Fedorov, Zening Fu, Vince D Calhoun, Sergey M Plis + + + Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Mon, 11 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Authors : Xinhui Li, Eloy Geenjaar, Zening Fu, Sergey Plis, Vince Calhoun + + + Statelets: Capturing recurrent transient variations in dynamic functional network connectivity + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Wed, 01 Jun 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Authors : Md Abdur Rahaman, Eswar Damaraju, Debbrata K Saha, Sergey M Plis, Vince D Calhoun + + + Constraint-Based Causal Structure Learning from Undersampled Graphs + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Wed, 18 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Authors : Mohammadsajad Abavisani, David Danks, Sergey Plis Publication date : 2022/5/18 + + + Privacy‐preserving quality control of neuroimaging datasets in federated environments + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Sun, 01 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Authors : Debbrata K Saha, Vince D Calhoun, Yuhui Du, Zening Fu, Soo Min Kwon, Anand D Sarwate, Sandeep R Panta, Sergey M Plis + + + Decentralized Brain Age Estimation using MRI Data + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Tue, 05 Apr 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Authors : Sunitha Basodi, Rajikha Raja, Bhaskar Ray, Harshvardhan Gazula, Anand D Sarwate, Sergey Plis, Jingyu Liu, Eric Verner, Vince D Calhoun + + + Geometrically Guided Saliency Maps + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Sat, 05 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Authors : Md Mahfuzur Rahman, Noah Lewis, Sergey Plis Publication date : 2014/3/1 + + + Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Tue, 01 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Authors : Shile Qi, Rogers F Silva, Daoqiang Zhang, Sergey M Plis, Robyn Miller, Victor M Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D Calhoun + + + Deep learning in neuroimaging: Promises and challenges + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Thu, 24 Feb 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Authors : Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M Plis, Yu-Ping Wang, Jing Sui, Vince D Calhoun + + + Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium + + + Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Authors : Haleh Falakshahi, Hooman Rokham, Zening Fu, Armin Iraji, Daniel H Mathalon, Judith M Ford, Bryon A Mueller, Adrian Preda, Theo GM van Erp, Jessica A Turner, Sergey Plis, Vince D Calhoun + + + Single-shot pruning for offline reinforcement learning + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Fri, 31 Dec 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Authors : Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Doina Precup + + + Federated analysis of neuroimaging data: A review of the field + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Mon, 22 Nov 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + Tracking whole-brain connectivity dynamics in the resting state + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Sat, 01 Mar 2014 05:00:00 +0000 + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + diff --git a/tags/paper/page/1/index.html b/tags/paper/page/1/index.html new file mode 100644 index 0000000..a7ea833 --- /dev/null +++ b/tags/paper/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://neuroneural.github.io/tags/paper/ + + + + + + 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    Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains

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    + Sergey Plis | + Jan 1, 2022 | + Publications + +
    +

    Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium

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spectrum of Alzheimer's phenotypes + https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Wed, 07 Sep 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/self-supervised-multimodal-neuroimaging-yields-predictive-representations-for-a-spectrum-of-alzheimers-phenotypes/ + Authors : Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P DeRamus, Margaux Luck, Maria Misiura, R Devon Hjelm, Sergey M Plis, Vince D Calhoun + + + Pipeline-Invariant Representation Learning for Neuroimaging + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Sat, 27 Aug 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/pipeline-invariant-representation-learning-for-neuroimaging/ + Authors : Xinhui Li, Alex Fedorov, Mrinal Mathur, Anees Abrol, Gregory Kiar, Sergey Plis, Vince Calhoun + + + Interpreting models interpreting brain dynamics + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Thu, 21 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/interpreting-models-interpreting-brain-dynamics/ + Authors : Md Rahman, Usman Mahmood, Noah Lewis, Harshvardhan Gazula, Alex Fedorov, Zening Fu, Vince D Calhoun, Sergey M Plis + + + Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Mon, 11 Jul 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/mind-the-gap-functional-network-connectivity-interpolation-between-schizophrenia-patients-and-controls-using-a-variational-autoencoder/ + Authors : Xinhui Li, Eloy Geenjaar, Zening Fu, Sergey Plis, Vince Calhoun + + + Statelets: Capturing recurrent transient variations in dynamic functional network connectivity + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Wed, 01 Jun 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/statelets-capturing-recurrent-transient-variations-in-dynamic-functional-network-connectivity/ + Authors : Md Abdur Rahaman, Eswar Damaraju, Debbrata K Saha, Sergey M Plis, Vince D Calhoun + + + Constraint-Based Causal Structure Learning from Undersampled Graphs + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Wed, 18 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/constraint-based-causal-structure-learning-from-undersampled-graphs/ + Authors : Mohammadsajad Abavisani, David Danks, Sergey Plis Publication date : 2022/5/18 + + + Privacy‐preserving quality control of neuroimaging datasets in federated environments + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Sun, 01 May 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/privacypreserving-quality-control-of-neuroimaging-datasets-in-federated-environments/ + Authors : Debbrata K Saha, Vince D Calhoun, Yuhui Du, Zening Fu, Soo Min Kwon, Anand D Sarwate, Sandeep R Panta, Sergey M Plis + + + Decentralized Brain Age Estimation using MRI Data + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Tue, 05 Apr 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/decentralized-brain-age-estimation-using-mri-data/ + Authors : Sunitha Basodi, Rajikha Raja, Bhaskar Ray, Harshvardhan Gazula, Anand D Sarwate, Sergey Plis, Jingyu Liu, Eric Verner, Vince D Calhoun + + + Geometrically Guided Saliency Maps + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Sat, 05 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/geometrically-guided-saliency-maps/ + Authors : Md Mahfuzur Rahman, Noah Lewis, Sergey Plis Publication date : 2014/3/1 + + + Three‐way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Tue, 01 Mar 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/three-way-parallel-group-independent-component-analysis-fusion-of-spatial-and-spatiotemporal-magnetic-resonance-imaging-data/ + Authors : Shile Qi, Rogers F Silva, Daoqiang Zhang, Sergey M Plis, Robyn Miller, Victor M Vergara, Rongtao Jiang, Dongmei Zhi, Jing Sui, Vince D Calhoun + + + Deep learning in neuroimaging: Promises and challenges + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Thu, 24 Feb 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/deep-learning-in-neuroimaging-promises-and-challenges/ + Authors : Weizheng Yan, Gang Qu, Wenxing Hu, Anees Abrol, Biao Cai, Chen Qiao, Sergey M Plis, Yu-Ping Wang, Jing Sui, Vince D Calhoun + + + Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-in-coinstac-reveals-functional-network-connectivity-and-spectral-links-to-smoking-and-alcohol-consumption-in-nearly-2000-adolescent-brains/ + Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium + + + Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Sat, 01 Jan 2022 05:00:00 +0000 + https://neuroneural.github.io/posts/path-analysis-a-method-to-estimate-altered-pathways-in-time-varying-graphs-of-neuroimaging-data/ + Authors : Haleh Falakshahi, Hooman Rokham, Zening Fu, Armin Iraji, Daniel H Mathalon, Judith M Ford, Bryon A Mueller, Adrian Preda, Theo GM van Erp, Jessica A Turner, Sergey Plis, Vince D Calhoun + + + Single-shot pruning for offline reinforcement learning + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Fri, 31 Dec 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/single-shot-pruning-for-offline-reinforcement-learning/ + Authors : Samin Yeasar Arnob, Riyasat Ohib, Sergey Plis, Doina Precup + + + Federated analysis of neuroimaging data: A review of the field + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Mon, 22 Nov 2021 05:00:00 +0000 + https://neuroneural.github.io/posts/federated-analysis-of-neuroimaging-data-a-review-of-the-field/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + Tracking whole-brain connectivity dynamics in the resting state + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Sat, 01 Mar 2014 05:00:00 +0000 + https://neuroneural.github.io/posts/tracking-whole-brain-connectivity-dynamics-in-the-resting-state/ + Authors : Elena A Allen, Eswar Damaraju, Sergey M Plis, Erik B Erhardt, Tom Eichele, Vince D Calhoun + + + diff --git a/tags/publications/page/1/index.html b/tags/publications/page/1/index.html new file mode 100644 index 0000000..a547b94 --- /dev/null +++ b/tags/publications/page/1/index.html @@ -0,0 +1,10 @@ + + + + https://neuroneural.github.io/tags/publications/ + + + + + + diff --git a/tags/publications/page/2/index.html b/tags/publications/page/2/index.html new file mode 100644 index 0000000..9258ae7 --- /dev/null +++ b/tags/publications/page/2/index.html @@ -0,0 +1,486 @@ + + +Publications + + + + + + + + + + + + + + + + + + + + + +Publications + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Federated analysis in COINSTAC reveals functional network connectivity and spectral links to smoking and alcohol consumption in nearly 2,000 adolescent brains

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    + Sergey Plis | + Jan 1, 2022 | + Publications + +
    +

    Authors : Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun LW Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean-Luc Martinot, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Tomáš Paus, Luise Poustka, Juliane H Fröhner, Lauren Robinson, Michael N Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A Turner, Anand D Sarwate, Sergey M Plis, Vivek Benegal, Gunter Schumann, Vince D Calhoun, IMAGEN Consortium

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    Research Summary

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    I am interested in the following topics (with selected works):

    + +

    I also worked on Reinforcement Learning [NeurIPS Deep RL Workshop 2015] and Computer Vision problems (traffic sign recognition; age and gender recognition from human faces).

    +

    I am a Ph.D. student in Electrical & Computer Engineering at Georgia Institute of Technology under the supervision of Dr. Sergey Plis and Dr. Vince D. Calhoun. My research interests are Representation Learning, Self-Supervision, and Multimodal Learning.

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    Alex Fedorov

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    + I am a third year Ph.D. student in Electrical & Computer Engineering at +Georgia Institute of Technology +under the supervision of +Dr. Sergey Plis +and +Dr. Vince D. Calhoun +. I have interned at +Microsoft Research Redmond +and +Mila - Quebec AI Institute +
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    +
    Georgia Tech :
    +
    afedorov@gatech.edu
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    + +
    +
    LinkedIn :
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    https://www.linkedin.com/in/entodi/
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/team/alexandre-castelnau/index.html b/team/alexandre-castelnau/index.html new file mode 100644 index 0000000..ded52ad --- /dev/null +++ b/team/alexandre-castelnau/index.html @@ -0,0 +1,347 @@ + + +Alexandre Castelnau + + + + + + + + + + + + + + + + + + + + + +Alexandre Castelnau + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Alexandre Castelnau

    + +
    +
    + Hi everyone, my name is Alexandre Castelnau. I am a French graduate student enrolled in a dual degree program between Georgia Tech and CentraleSupélec, graduate engineering school of the Université Paris-Saclay. +
    + +
    +
    GSU :
    +
    acastelnau@gsu.edu
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    + +
    +
    Georgia Tech :
    +
    alexandre.castelnau@gatech.edu
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    + +
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/team/brad-baker/index.html b/team/brad-baker/index.html new file mode 100644 index 0000000..a7ee302 --- /dev/null +++ b/team/brad-baker/index.html @@ -0,0 +1,342 @@ + + +Brad Baker + + + + + + + + + + + + + + + + + + + + + +Brad Baker + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Brad Baker

    + +
    +
    + I am primarily interested in machine learning and its intersections with complex applications and theory. My current research interests are focused in leveraging insights from optimization and neural computation to interpret and innovate on Artificial Neural Networks. I am interested in novel methods for applying deep learning to neuroimaging data, especially drawing from distributed learning for performing efficient and privacy sensitive analyses in large scale, collaborative settings. I am additionally interested in the use of information theory for training and interpreting neural networks, the application of complex network theory principles to modelling neural dynamics, and drawing inspiration from neuroscience to innovate with artificial neural networks and vice-versa. +
    + +
    +
    GSU :
    +
    bbaker43@gsu.edu
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    + +
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/team/capella-edwards/index.html b/team/capella-edwards/index.html new file mode 100644 index 0000000..329e37f --- /dev/null +++ b/team/capella-edwards/index.html @@ -0,0 +1,337 @@ + + +Capella Edwards + + + + + + + + + + + + + + + + + + + + + +Capella Edwards + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Capella Edwards

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    + I’m enthusiastic about deep learning and how its applications can help people, especially in neuroimaging and other medical contexts. My current area of focus is on diagnostics with fMRI data. +
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/team/farfalla-hu/index.html b/team/farfalla-hu/index.html new file mode 100644 index 0000000..b5d6b61 --- /dev/null +++ b/team/farfalla-hu/index.html @@ -0,0 +1,342 @@ + + +Farfalla Hu + + + + + + + + + + + + + + + + + + + + + +Farfalla Hu + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    +

    Farfalla Hu

    + +
    +
    + Hi, everyone. My name is Farfalla Hu. I am a multimedia designer and I studied Motion Media and Interactive Design as my undergrad at SCAD. My master’s degree is at GSU in Computer Science. Here is my personal website http://farfallahu.com/. +
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    Eail :
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    FarfallaHu@gmail.com
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    + thumbnail-image +
    +

    Alex Fedorov

    +

    I am a third year Ph.D. student in Electrical & Computer Engineering at +[Georgia Institute …

    + Read More +
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    + +
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    + thumbnail-image +
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    Mohamed Masoud

    +

    Dr. Masoud is currently a postdoctoral researcher at Trends center, he received his PhD in …

    + Read More +
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    + thumbnail-image +
    +

    Minoo Jafarlou

    +

    Minoo Jafarlou is a Ph.D. student in Computer Science at Georgia State University. She …

    + Read More +
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    + thumbnail-image +
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    Mrinal Mathur

    +

    I am a Research assistant at TreNDs. I work on building novel research prototypes at the …

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    + thumbnail-image +
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    Pavel Popov

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    Hi everyone! I got bachelor's and master's degrees in physics and mathematics at the …

    + Read More +
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    + thumbnail-image +
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    Pratyush Reddy

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    I am an AWS Certified Solution Architect with experience as a Python developer, 4 years of …

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    + thumbnail-image +
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    Riyasat Ohib

    +

    I am a Ph.D. student at the Georgia Institute of Technology in the department of …

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    + thumbnail-image +
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    Sajad Abavisani

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    I am a third year Ph.D. student in Electrical and Computer Engineering Department of …

    + Read More +
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    + thumbnail-image +
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    Usman Mahmood

    +

    I am pursuing a Ph.D. with a concentration in machine learning, working on NeuroImages …

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    + thumbnail-image +
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    William Ashbee

    +

    William Stewart Ashbee is a doctoral student at Department of Computer Science at Georgia …

    + Read More +
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    + thumbnail-image +
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    Yaorong Xiao

    +

    I am interested in machine learning model optimization, multimodal data analysis, imaging …

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    + thumbnail-image +
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    Zafar Iqbal

    +

    My research is primarily focussed on applying deep learning neural networks to …

    + Read More +
    +
    +
    + +
    +
    + thumbnail-image +
    +

    Alexandre Castelnau

    +

    Hi everyone, my name is Alexandre Castelnau. I am a French graduate student enrolled in a …

    + Read More +
    +
    +
    + +
    +
    + thumbnail-image +
    +

    Farfalla Hu

    +

    Hi, everyone. My name is Farfalla Hu. I am a multimedia designer and I studied Motion …

    + Read More +
    +
    +
    + +
    +
    + thumbnail-image +
    +

    Brad Baker

    +

    I am primarily interested in machine learning and its intersections with complex …

    + Read More +
    +
    +
    + +
    +
    + thumbnail-image +
    +

    Capella Edwards

    +

    I’m enthusiastic about deep learning and how its applications can help people, especially …

    + Read More +
    +
    +
    + +
    +
    + thumbnail-image +
    +

    Md Mahfuzur Rahman

    +

    I am a Ph.D. candidate in Computer Science at Georgia State University. My research …

    + Read More +
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    + thumbnail-image +
    +

    Kseniya Solovyeva

    +

    I have completed my PhD in Biophysics and Masters in Applied Physics and Mathematics in …

    + Read More +
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/team/index.xml b/team/index.xml new file mode 100644 index 0000000..ab5385c --- /dev/null +++ b/team/index.xml @@ -0,0 +1,138 @@ + + + + Team on neuroneural | Sergey Plis, Ph.D. + https://neuroneural.github.io/team/ + Recent content in Team on neuroneural | Sergey Plis, Ph.D. + Hugo -- gohugo.io + en-us + Fri, 11 Nov 2022 05:00:00 +0000 + + + Alex Fedorov + https://neuroneural.github.io/team/alex-fedorov/ + Fri, 11 Nov 2022 05:00:00 +0000 + https://neuroneural.github.io/team/alex-fedorov/ + Research Summary I am interested in the following topics (with selected works): + + + Mohamed Masoud + https://neuroneural.github.io/team/mohamed-masoud/ + Tue, 07 Jul 2020 05:00:00 +0000 + https://neuroneural.github.io/team/mohamed-masoud/ + + + + Minoo Jafarlou + https://neuroneural.github.io/team/minoo-jafarlou/ + Sun, 07 Jun 2020 05:04:00 +0000 + https://neuroneural.github.io/team/minoo-jafarlou/ + + + + Mrinal Mathur + https://neuroneural.github.io/team/mrinal-mathur/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/mrinal-mathur/ + My research interest includes: Computer Vision Natural Language Generation/Understanding Multimodal Understanding Reinforcement Learning Causal/Counterfactual modelling Time Series Forecasting Multitask Learning I am pursuing Masters in Computer Science (specializing in Machine Learning) under Dr. + + + Pavel Popov + https://neuroneural.github.io/team/pavel-popov/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/pavel-popov/ + + + + Pratyush Reddy + https://neuroneural.github.io/team/pratyush-reddy/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/pratyush-reddy/ + + + + Riyasat Ohib + https://neuroneural.github.io/team/riyasat-ohib/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/riyasat-ohib/ + + + + Sajad Abavisani + https://neuroneural.github.io/team/sajad-abavisani/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/sajad-abavisani/ + + + + Usman Mahmood + https://neuroneural.github.io/team/usman-mahmood/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/usman-mahmood/ + + + + William Ashbee + https://neuroneural.github.io/team/william-ashbee/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/william-ashbee/ + + + + Yaorong Xiao + https://neuroneural.github.io/team/yaorong-xiao/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/yaorong-xiao/ + + + + Zafar Iqbal + https://neuroneural.github.io/team/zafar-iqbal/ + Fri, 07 Aug 2020 05:00:00 +0000 + https://neuroneural.github.io/team/zafar-iqbal/ + + + + Alexandre Castelnau + https://neuroneural.github.io/team/alexandre-castelnau/ + Sat, 07 Mar 2020 05:00:00 +0000 + https://neuroneural.github.io/team/alexandre-castelnau/ + + + + Farfalla Hu + https://neuroneural.github.io/team/farfalla-hu/ + Sat, 07 Mar 2020 05:00:00 +0000 + https://neuroneural.github.io/team/farfalla-hu/ + + + + Brad Baker + https://neuroneural.github.io/team/brad-baker/ + Fri, 11 Nov 2022 05:00:00 +0000 + https://neuroneural.github.io/team/brad-baker/ + + + + Capella Edwards + https://neuroneural.github.io/team/capella-edwards/ + Fri, 11 Nov 2022 05:00:00 +0000 + https://neuroneural.github.io/team/capella-edwards/ + + + + Md Mahfuzur Rahman + https://neuroneural.github.io/team/md-mahfuzur-rahman/ + Thu, 07 May 2020 05:00:00 +0000 + https://neuroneural.github.io/team/md-mahfuzur-rahman/ + + + + Kseniya Solovyeva + https://neuroneural.github.io/team/kseniya-solovyeva/ + Tue, 07 Apr 2020 05:00:00 +0000 + https://neuroneural.github.io/team/kseniya-solovyeva/ + + + + diff --git a/team/kseniya-solovyeva/index.html b/team/kseniya-solovyeva/index.html new file mode 100644 index 0000000..f358d76 --- /dev/null +++ b/team/kseniya-solovyeva/index.html @@ -0,0 +1,351 @@ + + +Kseniya Solovyeva + + + + + + + + + + + + + + + + + + + + + +Kseniya Solovyeva + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Kseniya Solovyeva

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    + I have completed my PhD in Biophysics and Masters in Applied Physics and Mathematics in Moscow Institute of Physics and Technology, Russia. My research interests center on the mechanisms of the brain and consciousness. My main goal is to create a technology for deliberately changing the state of consciousness to achieve states most suitable for effective work, learning, or therapy. I believe that we need high-quality brain models to reach this goal. For my PhD, I used computational modeling based on Hopfield networks, McCulloch-Pitt’s neurons, LIF, and Hodgkin-Huxley neurons. In addition, I proposed elements of communication between the architecture of artificial networks and the mechanisms of the brain. For the last couple of years, I have devoted myself to working with experimental data. My experience: +Experiments with EEG (dry and wet electrodes): solving cognitive tests, meditation, solving problems in mathematics, perception of visual images. +EEG data processing: spectral analysis, functional connectivity analysis, evoked potentials, elements of machine learning, calculation of information metrics (lempel-ziv, entropy). +Currently I am working on “Causal modeling of brain dynamical data with the goal of eventually arriving at better computational models of how the brain works.” +
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    GSU :
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    ksoloveva@gsu.edu
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    Md Mahfuzur Rahman

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    + I am a Ph.D. candidate in Computer Science at Georgia State University. My research interests primarily lie in the shared space of XAI, medical imaging, and computer vision.  In my ongoing research, I mainly focus on developing machine learning/deep learning models and leveraging explainable AI to advance our understanding of brain disorders.  I put my endeavor into creating new learning algorithms, explanation methods, metrics, and frameworks useful to build interpretable AI models and apply them to broaden our understanding in the neuroscience space. +
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    Minoo Jafarlou

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    + Minoo Jafarlou is a Ph.D. student in Computer Science at Georgia State University. She works as a graduate research assistant at TReNDS center. Her educational background is in computer engineering (BS) and computer science (MS). Minoo is interested in multimodal neuroimaging, deep learning, and machine learning. She is currently working on Few-Shot Learning. +
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    Mohamed Masoud

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    + Dr. Masoud is currently a postdoctoral researcher at Trends center, he received his PhD in computer science from Georgia State University. His research interest centers around the intersection region between 2D/3D/4D/multichannel Image Processing, Data Science (DS) and Web technologies to develop next generation web-based cutting-edge computational image analysis methods and tools. +
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    My research interest includes:

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      1. Computer Vision
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    2. Natural Language Generation/Understanding
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    3. Multimodal Understanding
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    4. Reinforcement Learning
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    5. Causal/Counterfactual modelling
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    6. Time Series Forecasting
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    7. Multitask Learning
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    I am pursuing Masters in Computer Science (specializing in Machine Learning) under Dr. Sergey Plis.

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    I worked as a Machine Learning Engineer for 3 years at ARM, where i was a part of Machine Learning group working in the Arm’s Machine Learning Research Lab under Parth Maj where we worked on many Deep Learning problems thath were used to train, optimize and accelerate NPU chips.

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    I earned a Bachelor’s degree in Computer Science in 2018 from Manipal Institute of Technology, where I was advised by Dr. Chetna Sharma. and worked on my thesis “Resume Parser for Blockchain Profile Using Novel Deep Learning”

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    Mrinal Mathur

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    + I am a Research assistant at TreNDs. I work on building novel research prototypes at the intersection of computer vision, natural language, and Reinforcement Learning. +I was also Teaching assistant and co-tutor for Intro to Deep Learning 6851 (Spring Semester 2022) +
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    Pavel Popov

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    + Hi everyone! I got bachelor’s and master’s degrees in physics and mathematics at the Moscow Institute of Physics and Technology, after which I decided to aspire to a Ph.D. in Computer science and Machine Learning at Georgia State University. My current research interests lie within the applications of Machine Learning to Neuroscience problems, mainly brain connectivity. +
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    GDU :
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    ppopov1@gsu.edu
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    Pratyush Reddy

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    + I am an AWS Certified Solution Architect with experience as a Python developer, 4 years of experience in the industry, currently pursuing masters in computer science at Georgia State University. Highly adept at handling various responsibilities by prioritizing necessary tasks, establishing clear deadlines and finding creative solutions to eliminate obstacles. +
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    Email :
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    pratyushrg@gmail.com
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    GDU :
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    pgaggenapalli1@student.gsu.edu
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    Riyasat Ohib

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    + I am a Ph.D. student at the Georgia Institute of Technology in the department of Electrical and Computer Engineering. I am primarily interested in Machine Learning and Deep Learning, focusing in the areas of sparsity in deep learning, efficient ML and optimization. Currently, I am working on developing novel methods of inducing sparsity in deep neural networks and studying the effects it has in the models. More recently, I have also started working in the areas of sparse reinforcement learning and multi-task learning. +
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    Email :
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    riyasat.ohib@gatech.edu
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    Sajad Abavisani

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    + I am a third year Ph.D. student in Electrical and Computer Engineering Department of Georgia Institute of Technology. My area of research incudes Machine Learning, Deep Learning and Causal Learning. I work on developing novel machine learning methods to extract causal relations among complex and big scale data from time series. Currently I am working on a new method for causal structure discovery. I improved state-of-the-art run-time by three orders of magnitude by re-defining the problem as constraint optimization and solving using Answer Set Programming. This method is used in understanding causal relations of the brain from fMRI data, as well as simulated data. +
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    Email :
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    s.abavisani@gatech.edu
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    Usman Mahmood

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    + I am pursuing a Ph.D. with a concentration in machine learning, working on NeuroImages {fMRIs} to classify and predict brain disorders and estimate disorder-specific dynamic and directed graphs of the human brain. I am interested in creating ‘glass-box’ deep learning models that produce interpretable results. +
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    Email :
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    umahmood1@gsu.edu
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    William Ashbee

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    + William Stewart Ashbee is a doctoral student at Department of Computer Science at Georgia State University since Fall 2019. He is researching deep learning based brain surface reconstruction methods that produce surface meshes from MRI voxel images. At present, he has reviewed multiple methods and is preparing to combine and extend these methods for publication. This exciting area of research utilizes geometric deep learning and pytorch3d. He hopes to build a deep learning method to compete with Freesurfer. His advisor is Sergey Plis. +
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    Email :
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    wsashbee@gmail.com
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    Yaorong Xiao

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    + I am interested in machine learning model optimization, multimodal data analysis, imaging inpainting, and many other machine learning algorithms. I am currently working on ICA, applying entropy maximization as the objective of the training process. I am very happy to join any machine learning related research. +
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    Email :
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    yxiao11@student.gsu.edu
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    Zafar Iqbal

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    + My research is primarily focussed on applying deep learning neural networks to neuroimaging data. My current research is focussed on using pre-trained models trained on time direction of FMRI data to predict and classify different abnormalities in the brain. We believe such pertained models could help learn better the brain functions. Recurrent Neural Networks work well with time series data. The goal is to develop models by exploiting the capabilities of RNNS and observe their performance on benchmark medical datasets. +
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    Email :
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    ziqbal5@student.gsu.edu
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    Responsibility of Contributors

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