Dr. Ghulam Mujtaba
Assistant Professor
Data Science and Management
Anderson College of Business and Computing
Assistant Professor
Data Science and Management
Anderson College of Business and Computing
Dr. Ghulam Mujtaba is an Assistant Professor in Data Science and Management at the Anderson College of Business and Computing, Regis University. Prior to joining Regis, he was a postdoctoral researcher at West Virginia University, Morgantown, West Virginia, USA. Dr. Mujtaba brings extensive expertise in computer vision, deep learning, multimedia retrieval, and mobile computing.
Dr. Mujtaba's educational background includes a Ph.D. in Computer Engineering from Gachon University in Seoul, South Korea, where he focused on personalized multimedia generation for resource-constrained devices. During his Ph.D., he was also a visiting researcher at Sungkyunkwan University, Korea. He also holds a Master of Science in Computer Science from Indus University, Karachi, and a Bachelor of Science in Computer Science from the COMSATS Institute of Information Technology, Lahore, Pakistan.
I am always open to collaborations with researchers, industry professionals, and students who share a passion for advancing AI technology. Whether you're interested in joint research projects, industry partnerships, or exploring innovative applications of computer vision and deep learning, I welcome opportunities to work together on impactful solutions.
Video analysis, object detection, and visual understanding systems
Neural architectures, optimization, and efficient AI frameworks
Mobile Computing
Resource-constrained devices, mobile AI, and multimedia systems
Developing advanced techniques for intelligent video content analysis and automated summarization systems that can work efficiently on mobile and edge devices.
Creating novel neural network architectures and optimization techniques for visual understanding with focus on efficiency and real-world deployment.
Designing client-driven AI frameworks that enable sophisticated machine learning capabilities on resource-constrained mobile and IoT devices.
Applying AI and machine learning techniques to solve challenges in educational technology and healthcare systems, with focus on practical deployment.