Dr. Manar Samad

Assistant Professor of Computer Science

student on campus

Department of Computer Science 
Tennessee State University 
3500 John A Merritt Blvd
Nashville, TN 37209

Office : McCord Hall 005E (Main Campus)
Email : msamad[at]tnstate[dot]edu
Phone : (615) 963-5877
Homepage : https://sites.google.com/view/cidalab/home

Areas of Interest 
Machine learning, Deep Neural Networks, Computer Vision, Natural Language Processing, Human-Computer Interactions, Affective Computing, Health Informatics.

Education 
Post Doc Fellow, Geisinger Medical Center, Danville, PA, USA.
Ph.D., Old Dominion University, Norfolk, VA, USA.
M.Sc., University of Calgary, Calgary, AB, Canada.
B.Sc., Bangladesh University of Engineering and Tech., Bangladesh.

Biographical Sketch 
Dr. Samad received his Ph.D. degree from the Computer Vision Lab at Old Dominion University. He received the 2016 outstanding Ph.D. researcher award in the ECE department at Old Dominion University. Dr. Samad received his master's degree from the University of Calgary, Alberta, Canada, with a concentration in human-computer interaction (HCI). He worked as a post-doctoral research fellow at Geisinger medical center in health informatics and clinical decision optimization using electronic health records and machine learning. Dr. Samad is a multidisciplinary computer scientist with experience in data science, HCI, computer vision, and natural language processing. His current research interests include algorithms and optimization of unsupervised or self-supervised learning, computer vision, natural language processing, and explainable AI. NIH, NIFA, and DoD fund his current research.

Teaching Interests 
Machine learning, Advanced Applied Mathematics, Computer vision, Discrete mathematics, Programming language, Computer graphics, Digital logic design, Digital and wireless communication, Algorithms, Data structures.

Grants

  • Samad, M. (PI) ($80,000). Multi-source Image Distribution Alignment for Cross-Domain Computer Vision Applications - Amazon Research Grant (2023-2024)
  • Samad, M. (PI) (~$800,000). Mathematically Inspired Deep Representation Learning of Unlabeled Heterogeneous Data – ARFL, Department of Defense. (2023-2027)
  • Samad, M. (Co-PI) ($500,000). Strengthen Plant Biotech Program By Integrating Genome Editing and Artificial Intelligence Technologies in Tomato Projects - National Institute of Food and Agriculture/USDA (2022-2025).
  • Samad, M. (PI) ($421,700). Optimizing the Utility of Large Electronic Health Records Data in Data-Driven Health Research - National Library of Medicine/National Institute of Health (2020-2023).

Selected Publications

  • Kazijevs, M. and Samad, M. D., Deep imputation of missing values in time series health data: A review with benchmarking, Journal of Biomedical Informatics, 2023, (In press) https://doi.org/10.1016/j.jbi.2023.104440.
  • Samad, M. D., Abrar, S., & Diawara, N. (2022). Missing value estimation using clustering and deep learning within multiple imputation framework. Knowledge-Based Systems, 249, 108968. https://doi.org/10.1016/j.knosys.2022.108968 (IF: 8.0).
  • Abrar, S., & Samad, M. D. (2022). Perturbation of deep autoencoder weights for model compression and classification of tabular data. Neural Networks, 156, 160–169. https://doi.org/10.1016/j.neunet.2022.09.020 (IF: 10.0)
  • Kazijevs, M., & Samad, M. D. (2023). Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking. ArXiv Preprint ArXiv:2302.10902. Retrieved from http://arxiv.org/abs/2302.10902 (Under review)
  • Samad, M. D., Abrar, S., & Bataineh, M. (2023). G-CEALS: Gaussian Cluster Embedding in Autoencoder Latent Space for Tabular Data Representation. ArXiv Preprint ArXiv:2301.00802. Retrieved from http://arxiv.org/abs/2301.00802 (Under review)
  • Alam,  M. Samad,  L. Vidyaratne, A. Glandon, & K. Iftekharuddin, Survey on Deep Neural Networks in Speech and Vision Systems. Neurocomputing, Vol. 417, page 302-321, 2020 (Impact Factor: 5.71)
  • Baqui,  M. Samad,  Rainald Lohner, "A Novel Framework for Automated Monitoring and Analysis of High Density Pedestrian Flow" J. of Intelligent Transportation Systems: Technology, Planning, and Operations, Vol. 24, No 6, 2020 (Impact Factor: 4.23)
  • Reza, S. S.,  Samad, M. D.,  Shboul, Z. A., Jones, K. A., & Iftekharuddin, K. M. (2019). Glioma grading using structural magnetic resonance imaging and molecular data.  Journal of Medical Imaging,  6(02). (Impact Factor: 3.4)
  • Samad, M. D.,  Wehner, G. J., Arbabshirani, M. R., Jing, L., Powell, A. J., Geva, T., … Fornwalt, B. K. (2018). Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning. European Heart Journal Cardiovascular Imaging, 19(7), 730–738.(Impact Factor: 6.87)
  • Samad, M. D.,Ulloa, A., Wehner, G. J., Jing, L., Hartzel, D., Good, C. W., … Fornwalt, B. K. (2018). Predicting Survival From Large Echocardiography and Electronic Health Record Datasets. JACC: Cardiovascular Imaging, 12(4), 681–689. (Impact Factor: 14.2)