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Hire Dr. Hazem F.

Egypt

USD 50 /hr

Applied Deep Learning Scientist in Cyber-physical Systems

Profile Summary

I am a Research Scientist and deep-tech entrepreneur with over 10 years of R&D experience transforming complex machine learning prototypes into scalable, enterprise-ready software and embedded mechatronic systems. By leveraging Explainable AI (XAI), I engineer automated testing frameworks to expose hidden edge-case vulnerabilities in your AI models (such as computer vision and sensor pipelines) before they reach production. This ensures that your final deployments are compliant, functionally safe, and protected against costly real-world failures. With an h-index of 8, approximately 250 citations, and a continuous stream of publications in top-tier $Q1/A^*$ venues such as ACM TOSEM and IEEE TVT, I bridge the gap between advanced academic research and immediate commercial value. My domain expertise centers on de-risking autonomous cyber-physical systems (such as self-driving car perception pipelines) and FinTech architectures for functional safety and strict regulatory compliance (including ISO 26262, ISO/PAS 21448/SOTIF, GDPR, and the EU AI Act).

Subject Matter Expertise

• ★ Software Engineering
• ★ Automotive Engineering
• ★ Computer Vision
• ☆ Electrical Engineering
• ☆ Electronics Engineering
• ☆ Electronic Circuits
• ☆ Integrated Circuits/Microchips
• ☆ Microelectronics
• ☆ Computer Engineering
• ☆ Computer Science
• ☆ Software Architecture
• ☆ Robot Programming
• ☆ Mechanical Engineering
• ☆ Mechatronics & Robotics
• ☆ Data Analysis
• ☆ Image Processing
• ☆ Computer Simulation/Computational Modeling
• ☆ Algorithm Design
• ☆ Predictive Modeling & Analytics
• ☆ Data Processing
• ☆ Cluster Analysis
• ☆ Data Visualization
• ☆ Machine Learning
• ☆ Natural Language Processing
• ☆ Neural Networks
• ☆ Pattern Recognition
• ☆ Artificial Intelligence
• ☆ Deep Learning
• ☆ Statistics
• ☆ Regression Analysis
• ☆ Multivariate Analysis
• ☆ Statistical Computing
• ☆ Embedded Systems
• ☆ Risk Assessment & Actuarial Science
• ☆ Python
• ☆ Matlab
• ☆ Java
• ☆ Tensorflow
• ☆ LaTex
• ☆ Scikit-learn
• ☆ Markup Languages

Services

• Data & AI

Data & AI Predictive Modeling, Statistical Analysis, Image Analysis, Algorithm Design-ML

Work Experience

Zewail City of Science and Technology

- Present

Education

PhD

University of Luxemburg - Luxembourg

January 2019 - January 2023

Certifications

• Certification details not provided.

Publications

JOURNAL ARTICLE

Hazem Fahmy, Mohammed Oualid Attaoui, Fabrizio Pastore, Lionel Briand (2024). Supporting Safety Analysis of Image-processing DNNs through Clustering-based Approaches . ACM Transactions on Software Engineering and Methodology.

Hazem Fahmy, Fabrizio Pastore, Lionel Briand, Thomas Stifter (2023). Simulator-based Explanation and Debugging of Hazard-triggering Events in DNN-based Safety-critical Systems . ACM Transactions on Software Engineering and Methodology.

Hazem Fahmy, Mohammed Attaoui, Fabrizio Pastore, Lionel Briand (2023). Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering . ACM Transactions on Software Engineering and Methodology.

Hazem Fahmy, Fabrizio Pastore, Lionel Briand, Thomas Stifter(2022). Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems . ACM Transactions on Software Engineering and Methodology. Association for Computing Machinery ({ACM})

Hazem Fahmy, Mohammed Oualid Attaoui, Fabrizio Pastore, Lionel Briand (2022). Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering . ACM Transactions on Software Engineering and Methodology.

Hazem Fahmy, Fabrizio Pastore, Mojtaba Bagherzadeh, Lionel Briand(2021). Supporting Deep Neural Network Safety Analysis and Retraining Through Heatmap-Based Unsupervised Learning . IEEE Transactions on Reliability. 70. (4). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1641--1657. Institute of Electrical and Electronics Engineers ({IEEE})

Hazem Fahmy and Fabrizio Pastore and Mojtaba Bagherzadeh and Lionel Briand(2021). Supporting Deep Neural Network Safety Analysis and Retraining Through Heatmap-Based Unsupervised Learning . IEEE Transactions on Reliability. 70. (4). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1641--1657. Institute of Electrical and Electronics Engineers ({IEEE})

Fahmy, H., Pastore, F., Bagherzadeh, M., Briand, L.(2021). Supporting Deep Neural Network Safety Analysis and Retraining through Heatmap-Based Unsupervised Learning . IEEE Transactions on Reliability. 70. (4). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1641-1657.

Fahmy, H.M., Ghany, M.A.A.E., Baumann, G.(2018). Vehicle Risk Assessment and Control for Lane-Keeping and Collision Avoidance at Low-Speed and High-Speed Scenarios . IEEE Transactions on Vehicular Technology. 67. (6). Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 4806-4818.

OTHER

Attaoui, M.O., Fahmy, H., Pastore, F., Briand, L.(2023). DNN Explanation for Safety Analysis: An Empirical Evaluation of Clustering-based Approaches . arXiv.

Attaoui, M.O., Fahmy, H., Pastore, F., Briand, L.(2023). DNN Explanation for Safety Analysis: An Empirical Evaluation of Clustering-based Approaches . arXiv.

Hazem Fahmy, Fabrizio Pastore, Lionel Briand(2022). HUDD: A tool to debug DNNs for safety analysis . 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). {IEEE}

Fahmy, H., Pastore, F., Briand, L.(2022). HUDD: A tool to debug DNNs for safety analysis . arXiv.

Fahmy, H.M., Abd El Ghany, M.A., Baumann, G.(2022). Vehicle Risk Assessment and Control for Lane-Keeping and Collision Avoidance in Urban and Highway Driving Scenarios . arXiv.

Fahmy, H.M., Abd El Ghany, M.A., Baumann, G.(2022). Vehicle Risk Assessment and Control for Lane-Keeping and Collision Avoidance in Urban and Highway Driving Scenarios . arXiv.

Fahmy, H., Mahrajan, S.(2022). Vehicular Safety Applications and Approaches: A Technical Survey . arXiv.

Fahmy, H., Pastore, F., Briand, L., Stifter, T.(2022). Simulator-based explanation and debugging of hazard-triggering events in DNN-based safety-critical systems . arXiv.

Attaoui, M.O., Fahmy, H., Pastore, F., Briand, L.(2022). Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering . arXiv.

CONFERENCE PAPER

Hazem Fahmy and Fabrizio Pastore and Lionel Briand(2022). HUDD: A tool to debug DNNs for safety analysis . 2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). {IEEE}

Fahmy, H., Pastore, F., Briand, L.(2022). HUDD: A tool to debug DNNs for safety analysis . Proceedings - International Conference on Software Engineering. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 100-104.

Yousef, M., Hosny, A., Gamil, W., Adel, M., Fahmy, H.M., Saeed Darweesh, M., Mostafa, H.(2019). Dual-mode forward collision avoidance algorithm based on vehicle-to-vehicle (V2V) communication . Midwest Symposium on Circuits and Systems. 2018-August. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 739-742.

Fahmy, H.M., Baumann, G., Ghany, M.A.A.E., Mostafa, H.(2018). V2V-based vehicle risk assessment and control for lane-keeping and collision avoidance . Proceedings of the International Conference on Microelectronics, ICM. 2017-December. Microsoft.AspNetCore.Mvc.Localization.LocalizedHtmlString 1-5.