PhD in Computer Science | Associate Professor | Machine Learning Researcher

I bridge advanced machine learning research with practical, teachable applications. With a background in both academia (PhD, 10+ years teaching) and collaborative research (Laval University, Canada), I specialize in turning complex AI/ML concepts into working systems and clear explanations.

• 🔭 I’m currently working on: LLM applications (RAG systems, document Q&A), medical image classification, and time series forecasting.
• 🌱 I’m currently learning: MLOps, LLM deployment (LangChain, vector databases), and production AI.
• 👯 I’m looking to collaborate on: LLM research, open‑source AI tutorials, or industry internships.
• 💬 Ask me about: Machine Learning, Deep Learning (CNNs, LSTMs), LLMs and RAG, Big Data (Hadoop/Spark), and teaching data science.
• 📫 How to reach me: [Your Email] | [LinkedIn URL] | [Google Scholar URL]

• Document Q&A with RAG – Retrieval-Augmented Generation over PDF documents using LangChain + ChromaDB + OpenAI API (or local model)
• Research Paper Assistant – LLM-powered tool to summarize and answer questions about arXiv papers
• Code Documentation Generator – Uses an LLM to automatically generate docstrings and README files for Python code

• Chest X‑ray Classification – CNN models for multi-class chest X-ray analysis with Twitter API integration
• Skin Cancer Detection – Transfer learning for dermatological image classification

• Renewable Energy Forecasting – LSTM models for energy consumption prediction
• ECG Time Series Prediction – LSTM for electrocardiogram signal prediction

• Machine Learning Course – Graduate-level lectures, labs, and assignments
• Deep Learning Course – Neural networks from scratch to modern architectures