Rakib Al-Fahad

I started working at CVPIA lab from Fall 2013. I am pursuing my Ph.D. in Computer Engineering at the Department of Electrical and Computer Engineering at The University of Memphis. My research interest includes the following areas with the focus on cognitive neuroscience:

  • Exploratory data analysis, visualization and pattern analysis.
  • Feature selection in higher dimensional data with a limited sample size.
  • Classical machine learning, clustering, and regression analysis.
  • Connectivity analysis, visualization and graph mining.
  • Bayesian nonparametric methods for clustering and time series analysis.
  • Recurrent neural network analysis for time series prediction, classification, and forecasting.
  • Convolutional neural network, transfer learning, and generative adversarial networks.
  • Representations and visualization of visual concepts learned by convnets.
  • Big data analysis in a distributed computing system using Scala and Apache Spark.

Currently, I am working on the following projects:

Project: Modeling of Cognitive Performance

Early Imaging-Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL”: a collaboration project with St. Jude Children’s Research Hospital, Memphis, TN led by Dr. Mohammed Yeasin and Dr. Wilburn Reddick.

Project: Human Connectome Project

Find out individual difference form human brain connectivity using deep learning and graph mining approach led by Dr. Mohammed Yeasin and Dr. Abbas Babajani-Feremi.

Project: Epistemic state of mind and color of emotion

Modeling epistemic state of mind and color of emotion from the electroencephalogram (EEG) physiological data. This research is an integral part of the ongoing blind ambition project led by Dr. Mohammed Yeasin.


  • Al-Fahad, Rakib, Mohammed Yeasin, and Gavin M. Bidelman. “Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions.” Journal of Neural Engineering 17.1 (2020): 016045.
  • Al-Fahad, Rakib, et al. “Early Imaging-Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL.” IEEE Access 7 (2019): 146662-146674.
  • Al-Fahad, Rakib, and Mohammed Yeasin. “Micro-states based dynamic brain connectivity in understanding the commonality and differences in gender-specific emotion processing.” 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019.
  • Al-Fahad, R., Yeasin, M., Anam, A.I. and Elahian, B., 2017, May. Selection of stable features for modeling 4-D affective space from EEG recording. In Neural Networks (IJCNN), 2017 International Joint Conference on (pp. 1202-1209). IEEE.
  • Al-Fahad, R. and Yeasin, M., 2016, December. Robust modeling of continuous 4-d affective space from EEG recording. In Machine Learning and Applications (ICMLA), 2016 15th IEEE International Conference on (pp. 1040-1045). IEEE.
  • Ahmed, F., Mahmud, M. S., Al-Fahad, R., Alam, S., and Yeasin, M. 2018, April. Image Captioning for Ambient Awareness on a Sidewalk. In Data Intelligence and Security (ICDIS), 2018 1st International Conference on (pp. 85-91). IEEE.


  • Rakib Al-Fahad, M.Y, J Glass, H. Conklin, L. Jacola, W. Reddick, 2017. Early Imaging-Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL. In OHBM 2017, Poster Number: 3910
  • Rakib Al-Fahad, M.Y., 2016. What does Band Frequency Activities Tell us about the 4-D Affective Space? In OHBM 2016, Poster Number: 3395

Rakib Al-Fahad

Ph.D. Candidate
Electrical and Computer Engineering
The University of Memphis

Linkedin: https://www.linkedin.com/in/rakib-al-fahad-b4a50b18/

Github: https://github.com/rakibalfahad/