Khaled R. Ahmed

Khaled R. Ahmed is an Associate Professor and Graduate Program Director at the School of Computing, Southern Illinois University. He serves as Director of the Big Data & Software Engineering Lab (BASE Lab), focusing on software engineering support for big data systems and deep learning algorithms. His research bridges computer science with practical applications in agriculture, safety, and environmental monitoring.

Dr. Ahmed's research interests center on Big Data Software Engineering, Deep Learning, Parallel and Distributed Computing, and Computer Vision. His laboratory collaborates extensively with the agriculture college on precision agriculture projects, securing federal funding from USDA-NIFA for research on livestock monitoring and crop analysis. His work demonstrates a clear trajectory from foundational distributed systems research to applied deep learning solutions for real-world problems.

His recent publications reveal a strong focus on agricultural applications of computer vision, including weed detection systems, livestock methane emission monitoring, and soybean growth stage analysis. The BASE Lab has developed innovative approaches using Vision Transformers, DETR, and RetinaNet for precision agriculture applications, with several papers published in top venues like CVPR and IEEE Transactions.

• USDA-NIFA Research Award for Detecting Subacute Ruminal Acidosis
• USDA-NIFA Research Award for Artificial Intelligence for Greener Livestock
• Illinois Innovation Network Seed Grant
• Outstanding Achievement Award at World Congress in Computer Science (2020)

Dr. Ahmed has successfully mentored numerous graduate students, with several receiving Outstanding Thesis Awards. His BASE Lab actively recruits Ph.D. and MSc students with backgrounds in Deep Learning, Big Data, and Software Engineering. He maintains international connections through Fulbright programs, particularly with researchers from India and Egypt interested in AI and Deep Learning applications.

The BASE Lab maintains strong industry and research collaborations, particularly in precision agriculture where computer vision techniques are applied to solve practical farming challenges. Current projects include real-time video analysis systems for safety applications, drone-based monitoring for natural disaster recovery, and weapon detection systems for public safety.