2009 - IEEE Fellow For contributions to nonlinear and complex-valued statistical signal processing
2008 - Fellow of the Indian National Academy of Engineering (INAE)
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 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.
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.
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.
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)
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.
Estimating the number of independent components for functional magnetic resonance imaging data.
Yi Ou Li;Tülay Adali;Vince D. Calhoun.
Human Brain Mapping (2007)
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)
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.
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)
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
Dana Lahat;Tulay Adali;Christian Jutten.
Proceedings of the IEEE (2015)
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)
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)
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.
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.
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: