H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 65 Citations 19,426 306 World Ranking 1147 National Ranking 671

Research.com Recognitions

Awards & Achievements

2009 - IEEE Fellow For contributions to nonlinear and complex-valued statistical signal processing

2008 - Fellow of the Indian National Academy of Engineering (INAE)

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

The scientist’s investigation covers issues in Independent component analysis, Artificial intelligence, Pattern recognition, Functional magnetic resonance imaging and Algorithm. His Independent component analysis research integrates issues from Maximization, Blind signal separation, Higher-order statistics, Speech recognition and Mutual information. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision.

The various areas that Tulay Adali examines in his Pattern recognition study include Linear model, Probabilistic logic, Neuroimaging, Regression analysis and Voxel. The study incorporates disciplines such as Sensor fusion, Functional imaging and Brain mapping in addition to Functional magnetic resonance imaging. His Algorithm study combines topics from a wide range of disciplines, such as Signal processing, Nonlinear system, Complex conjugate, Perceptron and Multilayer perceptron.

His most cited work include:

  • A method for making group inferences from functional MRI data using independent component analysis (2101 citations)
  • A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. (805 citations)
  • The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery (738 citations)

What are the main themes of his work throughout his whole career to date?

His main research concerns Artificial intelligence, Independent component analysis, Pattern recognition, Algorithm and Functional magnetic resonance imaging. His studies deal with areas such as Machine learning, Blind signal separation and Computer vision as well as Artificial intelligence. He undertakes interdisciplinary study in the fields of Independent component analysis and Component through his research.

In his study, Kullback–Leibler divergence is strongly linked to Entropy, which falls under the umbrella field of Pattern recognition. His Algorithm research is multidisciplinary, incorporating perspectives in Nonlinear system, Mathematical optimization and Signal processing. Tulay Adali combines subjects such as Modality, Neuroimaging and Electroencephalography with his study of Functional magnetic resonance imaging.

He most often published in these fields:

  • Artificial intelligence (52.56%)
  • Independent component analysis (40.00%)
  • Pattern recognition (39.30%)

What were the highlights of his more recent work (between 2015-2021)?

  • Artificial intelligence (52.56%)
  • Independent component analysis (40.00%)
  • Pattern recognition (39.30%)

In recent papers he was focusing on the following fields of study:

Tulay Adali mainly focuses on Artificial intelligence, Independent component analysis, Pattern recognition, Functional magnetic resonance imaging and Blind signal separation. His Artificial intelligence study integrates concerns from other disciplines, such as Independence, Machine learning and Electroencephalography. His biological study spans a wide range of topics, including Data mining, Cluster analysis, Entropy, Feature extraction and Principal component analysis.

His study on Canonical correlation is often connected to Matrix decomposition as part of broader study in Pattern recognition. His research integrates issues of Modality, Voxel and Neuroimaging in his study of Functional magnetic resonance imaging. His study in Blind signal separation is interdisciplinary in nature, drawing from both Entropy maximization, Probability density function and Noise.

Between 2015 and 2021, his most popular works were:

  • Linked Component Analysis From Matrices to High-Order Tensors: Applications to Biomedical Data (120 citations)
  • Higher Dimensional Meta-State Analysis Reveals Reduced Resting fMRI Connectivity Dynamism in Schizophrenia Patients (108 citations)
  • A Shared Vision for Machine Learning in Neuroscience (60 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

The scientist’s investigation covers issues in Functional magnetic resonance imaging, Artificial intelligence, Independent component analysis, Pattern recognition and Neuroimaging. His Functional magnetic resonance imaging research is multidisciplinary, relying on both Image processing and Algorithm. His Artificial intelligence study combines topics in areas such as Big data, Machine learning, Human Brain Project and Blind signal separation.

His research on Independent component analysis often connects related topics like Voxel. Tulay Adali has researched Pattern recognition in several fields, including Data set, Sensor fusion and Signal processing. Tulay Adali has included themes like Modality and Electroencephalography in his Neuroimaging study.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A method for making group inferences from functional MRI data using independent component analysis

V. D. Calhoun;V. D. Calhoun;T. Adali;G. D. Pearlson;J. J. Pekar;J. J. Pekar.
Human Brain Mapping (2001)

2364 Citations

A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.

Vince D. Calhoun;Jingyu Liu;Jingyu Liu;Tülay Adalı.
NeuroImage (2009)

885 Citations

Estimating the number of independent components for functional magnetic resonance imaging data.

Yi Ou Li;Tülay Adali;Vince D. Calhoun.
Human Brain Mapping (2007)

733 Citations

Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms.

V.D. Calhoun;T. Adali;G.D. Pearlson;J.J. Pekar;J.J. Pekar.
Human Brain Mapping (2001)

716 Citations

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

Vince D. Calhoun;Vince D. Calhoun;Robyn Miller;Godfrey Pearlson;Tulay Adalı.
Neuron (2014)

705 Citations

Comparison of multi-subject ICA methods for analysis of fMRI data.

Erik Barry Erhardt;Srinivas Rachakonda;Edward J. Bedrick;Elena A. Allen.
Human Brain Mapping (2011)

539 Citations

Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects

Dana Lahat;Tulay Adali;Christian Jutten.
Proceedings of the IEEE (2015)

489 Citations

Multisubject Independent Component Analysis of fMRI: A Decade of Intrinsic Networks, Default Mode, and Neurodiagnostic Discovery

V. D. Calhoun;T. Adali.
IEEE Reviews in Biomedical Engineering (2012)

383 Citations

Complex-Valued Signal Processing: The Proper Way to Deal With Impropriety

T. Adali;P. J. Schreier;L. L. Scharf.
IEEE Transactions on Signal Processing (2011)

354 Citations

fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis.

V. D. Calhoun;V. D. Calhoun;T. Adali;V. B. McGinty;J. J. Pekar;J. J. Pekar.
NeuroImage (2001)

319 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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