Hello, I'm
Mahmoud Sameh
AI Researcher & Engineer
Electrical engineer with a deep focus on AI. I research, build, and ship. My work spans computer vision, foundation model adaptation, and applied ML across domains from fintech and healthcare to robotics and hyperspectral imaging.
Featured Projects
Selected work showcasing my engineering and problem-solving approach.
FragLibrary: An AI-Powered Fragrance Discovery Platform
Full-stack fragrance discovery platform (10,000+ fragrances) with a custom-trained embedding model (Scent2Vec 2.0) that maps scent preferences to closest matches via cosine similarity.
CircuitVision: AI-Powered Recognition of Hand-Drawn Electrical Circuits
End-to-end system converting hand-drawn circuit diagrams into simulatable netlists via fine-tuned YOLOv11 detection, SAM 2 segmentation, custom OpenCV connectivity, and Gemini API for value recognition. Awarded 2nd Best Graduation Project in AI university-wide.
Nathir: AI Legal Case Classification System
AI system for classifying legal cases on Saudi Arabia's Najiz platform using advanced prompt engineering with Google Gemini LLM, achieving 95% target accuracy. Co-winner of SDAIA's Enjaz Hackathon (150,000 SAR prize).
Publications
Research and academic contributions to the AI field.
Real-Time Human Fall Detection from Video Using YOLOv11 with Pose Estimation: A Paradigm Shift Towards Efficient Transformer-Based Architectures
A. BenAbdennour, M. Sameh, B. A. Khawaja, A. K. Vallappil, A. M. Alenezi, Q. H. Abbasi, S. Qazi
This paper presents a real-time fall detection system leveraging YOLOv11 with integrated pose estimation. By adopting an efficient transformer-based architecture, the system achieves high detection accuracy and low latency suitable for deployment in clinical and home monitoring environments, representing a significant advance over prior CNN-based approaches.
Predicting Emergency Department Patient Arrivals at Hospitals Using Machine Learning Techniques
A. M. Alenezi, M. Sameh, M. Aljohani, H. S. Alharbi
Accurately forecasting emergency department (ED) patient arrivals is critical for hospital resource planning and reducing patient wait times. This study applies and compares multiple machine learning techniques to predict daily and hourly ED arrival volumes, enabling proactive staffing and capacity management.
HSI Control Suite: An Integrated GUI for Operating and Acquiring Data from DIY Push-Broom Hyperspectral Imaging Systems
M. Sameh, A. Albeladi, A. Fawzy
This paper presents HSI Control Suite, an open-source Python GUI application (PyQt6) for end-to-end operation of DIY push-broom hyperspectral imaging (HSI) systems. The suite integrates camera control, stepper motor synchronization, data acquisition, spectral calibration, and hyperspectral cube assembly into a single accessible interface, lowering the barrier to entry for researchers building custom HSI hardware.
Career Journey
From foundations to AI, a timeline of growth.
Independent AI Researcher & Consultant
Shannah App · Independent
Conducting independent AI research and consulting for select projects. Advising Shannah, a pre-launch application currently under development, on its AI architecture and capabilities.
AI Engineer Intern
Aajil Fintech
Built AI infrastructure to automate credit risk assessment. Developed a feature-engineering pipeline generating financial analyses from raw application data. Trained and deployed classification/extraction models with end-to-end MLOps.
Research Intern
King Fahd University of Petroleum and Minerals
Designed a complete experimental protocol for a novel hyperspectral imaging dataset of date fruits. Developed the full software stack for push-broom HSI hardware and created an open-source GUI (PyQt6) now under journal review at SoftwareX.
B.S. Electrical Engineering
Islamic University of Madinah
Chose EE for its balance of hardware, software, and AI. Graduated top of class (4.95/5). Complemented formal education with Harvard's CS50 and self-driven courses in Python, C, and AI. Led and developed numerous projects across computer vision, robotics, and applied ML. Led technical aspects of multiple research papers as first or second author, won Enjaz Hackathon, earned 2nd Best Graduation Project in AI, and passed KAUST Academy's AI stages.
Preparatory Year
Islamic University of Madinah
Passed with a perfect 5/5 GPA without much effort, thanks to the self-study foundation built during the gap year (Thank you Khan Academy). Used the abundant free time to go deeper into calculus, linear algebra, and coding through online courses.
The Gap Year
Self-Directed
Took a deliberate year off to relearn mathematics the right way, revitalizing a love that high school methods had nearly killed. If you hate math, you're learning it wrong. Dove into JavaScript, computer science fundamentals, and psychology. Khan Academy was the star of that year: 1.9 million energy points earned through relentless dedication. A year where my love for coding and computers grew rapidly.
High School Graduate
Top 5 in Class
Graduated as one of the top 5 students. Left with a determination to learn differently, setting the stage for everything that followed.