Welcome to the Computer Vision Laboratory!

In the Computer Vision Laboratory, we conduct the-state-of-the-art image processing and deep learning research such as:

Signal and image processing for biomedical applications
Machine and deep learning applications
Edge AI optimization
Time-series analysis

Principal Investigator

Dr. Mohamed Shaban is a Tenured Associate Professor in the Electrical, and Computer Engineering department at the University of South Alabama (USA) with a joint appointment in the department of Pathology at the Whiddon College of Medicine. He is also the director of the Computer Vision Laboratory at USA. He has previously served as an Assistant Professor of Computer Science at Southern Arkansas University, Graduate Teaching Assistant at the University of Louisiana at Lafayette and an Assistant Lecturer at Mansoura University.

Dr. Shaban has received the Ph.D., and M.S. degrees in Computer Engineering from the University of Louisiana at Lafayette in 2016, and 2012 respectively. He has also received the M.S. degree in Electrical Communications Engineering, and the B.S. degree (Excellent with Honors Degree) in Electronics, and Communications Engineering from Mansoura University, Egypt in 2010, and 2006 respectively.

His current research interests are in the fields of Signal, and Image Processing for Biomedical Applications, Machine, and Deep Learning Applications, Edge Artificial Intelligence Optimization and Time-Series Analysis.

Current Students

Mohamed Salama

Mohamed Salama is a graduate assistant and a current member of the Computer Vision Laboratory. He is pursuing the Ph.D. in Systems Engineering (Electrical and Computer Engineering Track) at the University of South Alabama. He has completed the M.S. and B.S. degrees in Communication and Computer Engineering from Benha University, Egypt in 2024 and 2020 respectively. He has previously served as an Assistant Lecturer at the College of Engineering, Benha University, Egypt. He is currently investigating and proposing novel machine and deep learning methods for the prediction of the onset of geomagnetic substorms. His Ph.D. projects are sponsored by NASA.

Alumni

Md. Bipul Hossain, Ph.D. Thesis: “Attention-Based Deep-Learning Model Optimization for Low-Complexity Low-Memory Edge Devices”, April 2026.
Madeline Potter, Honors Thesis: “Deep Learning Based Detection of Breast Cancer in High Resolution Ultrasound and Histopathology Images”, April 2026.
Madan Parajuli, M.S. Thesis: “Deep-Learning Based Detection of Skin Cells in High-Resolution Histopathology Images for Melanoma Diagnosis”, September 2022.

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