Md Manjurul Ahsan

Dr. Md Manjurul Ahsan serves as a Research Assistant Professor in the Department of Industrial & Systems Engineering at the University of Oklahoma, where he develops AI-driven solutions for healthcare diagnostics and advanced manufacturing optimization. His work bridges theoretical AI advancements with practical industrial and medical applications.

Education:

• Ph.D. in Industrial and Systems Engineering, University of Oklahoma
• M.S. in Industrial Engineering, Lamar University
• B.S. in Industrial and Production Engineering, Shahjalal University of Science and Technology

Research Focus: Dr. Ahsan specializes in Artificial Intelligence with technical depth in Machine Learning, Deep Learning, and Computer Vision to solve critical challenges in healthcare diagnostics and additive manufacturing. His research emphasizes Explainable AI to enhance model trustworthiness and deployment efficiency across Cyber-Physical-Social Systems, with significant contributions to Aerospace and Defense applications.

Publication Trends: Recent work (2023-2025) reveals a strategic expansion from core manufacturing applications into medical AI (diffusion models for diagnostics), cultural preservation (NLP for Dravidian languages), and geopolitical AI analysis. His publications consistently address data imbalance challenges while advancing digital twin integration in quality control systems.

Scientific Recognition:

• GCOE Dissertation Excellence Award (2023)
• International Student Scholarship (2022)
• Outstanding Academic Achievement in Engineering (2022)
• IEEE IEMCON Best Paper Award (2020)
• Netti Vincent Boggs Engineering Excellence Award (2020)

Research Leadership: As director of the Sooner Additive Manufacturing Laboratory, Dr. Ahsan leads cross-disciplinary teams developing real-time monitoring systems using FARO arms and CMM metrology. His postdoctoral work at Northwestern University (2023-2024) advanced AI deployment frameworks, resulting in 60+ peer-reviewed publications with multiple papers ranking in engineering's top 1% for citations.