mohamad mehdi diab

Designed and delivered a 12-week AI in Healthcare course, building the full curriculum, labs, and supporting materials from scratch. Developed and instructed an Agentic AI Bootcamp, training participants to architect complex, data-driven AI agents and autonomous automation pipelines using n8n and LangChain nodes for real-world applications.

Built and launched a web platform that generates playable party-game cards using LLMs. Designed prompt pipelines and safety filters to improve output quality and reduce unsafe or low-quality generations, and shipped a full-stack freemium-to-premium product using Cursor, Claude Opus, and GPT-4o assistance.

Built and launched a public, copyright-free library of curated prompts for multiple AI models. Released open-source Stable Diffusion models and resources freely available to the community, and led a Discord community providing prompt guidance and AI technical support to thousands of users.

Contributed to the development of AI-driven tools supporting digital pathology workflows. Applied machine learning methods to assist diagnostic processes in neuro-oncology.

Researched and evaluated AI video generation models for fashion use cases (Veo, Kling, Runway, Hailuo, Seedance). Conducted prompt experimentation using GPT and Gemini to improve output controllability and visual consistency, and designed n8n automation workflows to streamline experimentation cycles and delivery pipelines.

Authored the Stable Diffusion Prompt Book and The Most Complete Guide to Stable Diffusion Parameters, and created prompt sets for the platform's photobooth feature. Benchmarked and evaluated LLMs (ChatGPT, Claude, DeepSeek, Gemini) for task-specific performance and contributed to community prompting initiatives as a designated Prompt Engineer.

Fine-tuned text-to-image models and curated prompt datasets for diverse generation use cases. Built and documented ComfyUI workflows to standardize evaluation and ensure output quality, and tested new generation techniques and optimized model parameters; acknowledged contributor to SDXL development and credited in the SDXL research paper.

Developed structured prompt systems to improve output consistency across Yokai.ai services. Fine-tuned LoRA models for targeted artistic styles and characters, iteratively improving generation quality.

One-year intensive program covering machine learning, deep learning, data science, AI for health, Arabic NLP, AI ethics, and data analytics for public policy.

Bachelor's degree in Medicine and Surgery, including clinical rotation at Alexandria University and postgraduate internship at Makassed General Hospital and Rafik Hariri University Hospital.