Muhammad Yousefnezhad, Ph.D.

2014-2018 Computer Science and Technology Nanjing University of Aeronautics and Astronautics, Nanjing Shi, Jiangsu Sheng, China 
 2019- Computing Science University of Alberta, Edmonton, Alberta, Canada 
Big data, deep learning, probabilistic graphical models, neuroimage analysis, health records analysis, bioinformatics, cybernetics
"Muhammad Yousefnezhad"

I am a postdoc fellow in the department of computing science and the department of psychiatry at the University of Alberta. My research interests lie in developing machine learning approaches with application to mental health data and neuroimaging such as fMRI, EEG, DTI, etc. Website: Blog:

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Sawalha J, Yousefnezhad M, Shah Z, et al. (2022) Detecting Presence of PTSD Using Sentiment Analysis From Text Data. Frontiers in Psychiatry. 12: 811392
Sawalha J, Yousefnezhad M, Selvitella AM, et al. (2021) Predicting pediatric anxiety from the temporal pole using neural responses to emotional faces. Scientific Reports. 11: 16723
Yousefnezhad M, Sawalha J, Selvitella A, et al. (2020) Deep Representational Similarity Learning for Analyzing Neural Signatures in Task-based fMRI Dataset. Neuroinformatics
Chen J, Tan J, Greenshaw AJ, et al. (2020) CACNB2 rs11013860 polymorphism correlates of prefrontal cortex thickness in bipolar patients with first-episode mania. Journal of Affective Disorders. 268: 82-87
Yousefnezhad M, Selvitella A, Han L, et al. (2020) Supervised Hyperalignment for multi-subject fMRI data alignment Ieee Transactions On Cognitive and Developmental Systems. 1-1
Yousefnezhad M, Zhang D. (2018) Multi-Objective Cognitive Model: a Supervised Approach for Multi-subject fMRI Analysis. Neuroinformatics
Yousefnezhad M, Huang SJ, Zhang D. (2017) WoCE: A framework for Clustering Ensemble by Exploiting the Wisdom of Crowds Theory. Ieee Transactions On Cybernetics
Yousefnezhad M, Zhang D. (2017) Anatomical Pattern Analysis for Decoding Visual Stimuli in Human Brains Cognitive Computation. 10: 284-295
Yousefnezhad M, Zhang D. (2016) Weighted spectral cluster ensemble Proceedings - Ieee International Conference On Data Mining, Icdm. 2016: 549-558
Yousefnezhad M, Reihanian A, Zhang D, et al. (2016) A new selection strategy for selective cluster ensemble based on Diversity and Independency Engineering Applications of Artificial Intelligence. 56: 260-272
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