معرفی
Hany Osman is an Associate Professor in the Master of Data Analytics program at the University of Niagara Falls Canada, holding a PhD in Industrial Engineering from Concordia University and a Professional Engineer (PEng) license in Ontario. His academic-industrial career bridges theoretical research with practical applications across multiple sectors.
Dr. Osman's research spans three interconnected domains:
- Machine Learning & Data Analytics: Specializing in logical analysis of data, cost-sensitive learning, and ensemble techniques for industrial applications
- Operations Research: Developing nature-inspired metaheuristics (cuckoo search, ant colony optimization) for NP-hard problems in manufacturing and logistics
- Supply Chain Management: Focusing on sustainable optimization of lot sizing, production planning, and inventory control under stochastic conditions
His recent publications (2023-2024) reveal a strategic pivot toward AI-integrated manufacturing systems, notably the CAPP-GPT framework for generative AI in process planning and emission-aware lot sizing models. This work demonstrates consistent translation of theoretical advances into industrial solutions for rail, oil, and smart manufacturing sectors.
Professional credentials include:
- IBM Mastery Certificate in Predictive Data Analytics
- Professional Engineer (PEng) license from Ontario
Dr. Osman leverages extensive industrial experience in supply chain logistics, oil industry optimization, and education technology to inform both research and teaching. His supervision in the Master of Data Analytics program emphasizes hands-on application of machine learning to real-world operational challenges, with students contributing to publications in Manufacturing Letters and related journals. While no formal lab is specified, his research group operates at the intersection of data science and industrial engineering, maintaining strong industry partnerships that drive applied projects.
