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. Graduated top of class. Four papers under review at IEEE and MDPI. Enjaz Hackathon national co-winner.
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.
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.
Efficiently Adapting SAM 2 for Automated Schematic Capture from Hand-Drawn Circuit Diagrams
M. Sameh, A. BenAbdennour, J. K. Ali
This paper proposes an efficient adaptation of Segment Anything Model 2 (SAM 2) for the task of automated schematic capture from hand-drawn electrical circuit diagrams. By fine-tuning SAM 2's video segmentation capabilities on circuit imagery, the system accurately segments and identifies components, enabling downstream netlist extraction and circuit simulation.
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.
AI Engineer
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 Assistant
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
Graduated top of class with a 4.95/5 GPA. Research across foundation model adaptation, computer vision, and applied ML. Four papers under review at IEEE TCAD, IEEE Access, SoftwareX, and MDPI Healthcare.
Student Contributor
IU Madinah — Engineering College
Managed college social media, led student teams on technical projects, and provided student-representative feedback on curriculum. Won Enjaz Hackathon and earned 2nd Best Graduation Project in AI university-wide.