Mohamed Hamed
Building intelligent systems at the intersection of AI and healthcare. Specializing in reinforcement learning, control systems, and full-stack software development.
Engineering with Purpose
Driven by curiosity and a commitment to building systems that matter.
I am a senior Biomedical Engineering graduate from Cairo University, Faculty of Engineering, specializing in Healthcare Engineering & Management. My work sits at the intersection of artificial intelligence, control systems, and software engineering.
My research focuses on applying reinforcement learning to biomedical control problems — most notably the development of an artificial pancreas system for automated insulin delivery. I approach complex problems with analytical rigor, combining theoretical depth with practical engineering.
Through my collaboration with Siemens, I have gained firsthand experience in industrial R&D workflows and have contributed to published research at international conferences. I am driven by the belief that intelligent systems can fundamentally improve healthcare outcomes.
Research-Driven
Published at DVCon Europe 2025. Experienced in designing experiments, modeling complex systems, and producing rigorous academic work.
AI & Systems Engineering
Deep expertise in reinforcement learning, control theory, and building intelligent systems that solve real-world biomedical challenges.
Industry Experience
Collaborated with Siemens on digital twin-based healthcare research, bridging the gap between academic research and industrial practice.
Full-Stack Development
Proficient across the software stack — from Python backends and FastAPI services to React frontends, Docker deployments, and cloud infrastructure.
Featured Work
A selection of real projects demonstrating expertise across AI, control systems, mobile development, and software engineering.
Language Breakdown
Research & Publications
Contributing to the advancement of digital twin technology, intelligent control, and AI-driven healthcare.
Leveraging Digital Twin Technology in Developing an Automated Type 1 Diabetes Insulin Administration System
M. Hamed et al.
This paper presents a novel approach to automated insulin delivery for Type 1 Diabetes by leveraging digital twin technology. A personalized physiological model of glucose-insulin dynamics is constructed as a digital twin, upon which reinforcement learning agents are trained to optimize real-time insulin dosing. The system demonstrates improved glycemic control and reduced hypoglycemia compared to conventional control strategies.
View PublicationResearch Areas
Reinforcement Learning for Control
Applying deep RL algorithms (PPO, SAC, TD3) to biomedical control problems, with focus on safety constraints and sim-to-real transfer.
Biomedical Systems Modeling
Mathematical modeling of physiological systems including glucose-insulin dynamics and cardiovascular mechanics.
Digital Twin Technology
Building high-fidelity virtual replicas of physiological systems for simulation, testing, and optimization of medical interventions and control strategies.
AI in Healthcare
Exploring applications of machine learning and AI in medical diagnostics, patient monitoring, and clinical decision support.
Technical Expertise
A comprehensive toolkit built through research, industry collaboration, and hands-on engineering.
Programming
AI & Machine Learning
Tools & Frameworks
Engineering
Education & Experience
A journey through academia, research, and industry collaboration.
BSc in Biomedical Engineering
Cairo University, Faculty of Engineering
Giza, Egypt
Healthcare Engineering & Management specialization. Comprehensive curriculum covering biomedical systems, control theory, electronics, and software engineering.
- Graduated with distinction
- Specialized in Healthcare Engineering & Management
- Graduation project on RL-based artificial pancreas system
- Strong foundation in mathematics, signal processing, and systems engineering
Research Collaborator — Digital Twin & AI for Healthcare
Siemens
Cairo, Egypt
Collaborated with Siemens on leveraging digital twin technology for automated Type 1 Diabetes insulin administration, resulting in a peer-reviewed publication at DVCon Europe 2025.
- Developed digital twin models of glucose-insulin dynamics for personalized simulation
- Co-authored conference paper accepted at DVCon Europe 2025
- Applied reinforcement learning for automated insulin dosing optimization
- Worked within professional R&D workflows and code review processes
Graduation Research — AI for Healthcare
Cairo University
Giza, Egypt
Led the design and development of a reinforcement learning-based artificial pancreas system for automated insulin delivery in Type 1 Diabetes management.
- Designed custom RL environments using the UVA/Padova T1D simulator
- Trained and evaluated PPO and SAC agents for glycemic control
- Implemented safety-constrained reward functions
- Conducted comprehensive comparison with classical PID controllers
Awards & Recognition
Milestones that reflect dedication to excellence in research and engineering.
DVCon Europe 2025 Publication
Co-authored and presented a peer-reviewed paper on leveraging digital twin technology for automated T1D insulin administration at DVCon Europe.
A+ on Graduation Project
Achieved the highest grade (A+) on the Artificial Pancreas System graduation project — an RL-based automated insulin delivery system using digital twin technology.
Let's Connect
Interested in collaboration, research opportunities, or just want to say hello? I'd love to hear from you.
I am actively looking for opportunities in AI engineering, software development, and intelligent systems. Open to full-time roles, collaborations, and interesting projects.