Ahmed Maged
Assistant Professor of Industrial Engineering
Dedicated to innovative research and practical applications in advanced industrial processes. Focused on Intelligent Manufacturing, Quality Engineering, Anomaly Detection and Deep Learning.
About Me
I am Ahmed Maged, an Assistant Professor of Industrial Engineering at the American University of Sharjah. My research focuses on Intelligent Manufacturing, Quality Engineering, Anomaly Detection and Deep Learning.
Education
Ph.D. in Systems Engineering
City University of Hong Kong, 2023
M.S. in Industrial Engineering
Benha University, 2017
B.S. in Mechanical Engineering
Benha University, 2013
Awards & Honors
Outstanding Academic Performance
City University of Hong Kong,2022
Research Tuition Scholarship
City University of Hong Kong,2021
Research Tuition Scholarship
City University of Hong Kong,2020
Best Paper Award
International Conference on Indsutrial Engineering and Operations Management, 2019
Excellence in Research
Benha University, 2018,2024
Research
My research focuses on developing advanced methodologies for industrial systems optimization, quality improvement, and intelligent manufacturing.
"Without data, you're just another person with an opinion."
Developing statistical and computational methods for quality control, process optimization, and robust design in manufacturing systems.
Creating advanced algorithms for early detection and accurate diagnosis of faults in complex industrial systems and manufacturing processes.
Applying deep learning and AI techniques for anomaly detection, predictive maintenance, and intelligent decision-making in manufacturing environments.
Publications
Selected peer-reviewed publications from my research.
Clinical Infectious Diseases, 2025
This multicenter longitudinal cohort study investigates the epidemiology of healthcare facility-associated nontuberculous mycobacteria (NTM) across a 10-hospital network in the United States between 2012 and 2020. The study provides detailed insights into infection trends, risk factors, and facility-level variations, offering evidence for improving infection prevention strategies in healthcare environments.
Composites Part C: Open Access, 2025
This study integrates deep learning and information fusion techniques to analyze the structure–property relationships in adhesive joints. The proposed framework combines multimodal data sources to enhance prediction accuracy of joint performance and mechanical behavior. The results demonstrate improved interpretability and reliability compared to traditional modeling methods.
Management Decision, 2025
This paper explores surveillance and monitoring mechanisms as key drivers for quality enhancement in organizational systems. It proposes an optimization scheme for a modified version EWMA chart (wEWMA).The proposed optimal wEWMA scheme also holds promise for broader application across other manufacturing sectors, including household consumer goods (e.g. home appliances) and industrial products (e.g. transformers, aluminium tubes, and printed circuit boards).
Biomedical Signal Processing and Control, 2025
This study presents an explainable framework for automatic brain abnormality detection in MRI images. The methodology includes a robust preprocessing pipeline and employs EfficientViT for classification, achieving up to 99.24% accuracy across various datasets. The model outperformed traditional deep learning techniques and provides explainable results to aid neurologists in diagnosis.
Fuel, 2025
This study uses deep learning to predict combustion pressure from flame images captured from a single-cylinder optical GDI engine. Five models were tested, with EfficientNetB4 achieving the best performance (R² of 0.94). Saliency analysis revealed the model detects subtle flame characteristics invisible to the human eye, advancing machine learning approaches for engine design and optimization.
Sustainable Production and Consumption, 2025
This study introduces a sustainable approach for monitoring manufacturing processes by optimizing the np chart to account for environmental and economic impacts. The optimal chart enhances detection of increasing shifts in nonconforming items, reducing emissions and costs. An application on fire extinguisher manufacturing demonstrated a 50% reduction in sustainability costs compared to conventional methods.
Courses
Courses I teach at the American University of Sharjah.
This course introduces students to the principles and techniques of quality engineering in manufacturing and service systems. It covers statistical process control, design of experiments, and quality improvement methodologies.
Key Topics:
- Statistical Process Control (SPC)
- Design of Experiments (DOE)
- Six Sigma Methodology
- Acceptance Sampling Plans
- Total Quality Management
This course provides students with hands-on experience using modern tools and platforms for big data processing and analysis. Students learn to work with large-scale datasets using distributed computing frameworks and cloud-based analytics solutions.
Key Topics:
- Big Data Platforms and Architecture
- Distributed Computing Frameworks
- Data Processing Pipelines
- Cloud-Based Analytics
- Real-Time Data Processing
This course covers the statistical principles and methodologies for designing and analyzing experiments in engineering and scientific research. Students learn to plan, conduct, and interpret experimental studies to optimize processes and products.
Key Topics:
- Factorial Designs
- Response Surface Methodology
- Taguchi Methods
- Fractional Factorial Designs
- Analysis of Variance (ANOVA)
Get in Touch
Interested in collaborating or have questions about my research? Feel free to reach out.
American University of Sharjah
Sharjah, UAE
