Senior Materials & Project Engineer specializing in applying AI/ML (PyTorch, Scikit-learn) to solve industrial challenges in the energy sector. I combine deep domain expertise in materials science with machine learning to create practical, data-driven solutions.

🎓 M.Eng. Mechanical & Materials Engineering - Western University, Ontario
🎓 AI/ML Certificate - Fanshawe College
💼 Focus Areas: Materials Informatics | Computer Vision for Quality Control | Predictive Modeling

I develop AI-powered tools that bridge the gap between materials science and industrial applications:

• Materials Property Prediction: Using machine learning to predict polymer properties and accelerate materials discovery
• Defect Detection Systems: Computer vision pipelines for automated quality control in manufacturing
• Data-Driven Materials Design: Applying AI to optimize material selection and performance prediction

Machine Learning & AI

Data Science & Visualization

Tools & Platforms

Core Competencies

✓ Deep Learning (CNNs, Transfer Learning, Computer Vision)
✓ Classical ML (XGBoost, Random Forests, SVMs, Ensemble Methods)
✓ Materials Informatics & Computational Materials Science
✓ Computer Vision (OpenCV, Image Processing, Defect Detection)
✓ Feature Engineering & Model Optimization
✓ End-to-End ML Pipeline Development

A materials informatics project using XGBoost and end-to-end MLOps pipeline to predict key polymer properties. Includes live interactive demo on Hugging Face Spaces.

Tech: Python, XGBoost, Scikit-learn, Hugging Face

End-to-end deep learning pipeline using computer vision to automatically detect and classify manufacturing defects on steel surfaces for industrial quality control.

Tech: Python, PyTorch, Computer Vision, Deep Learning

• Advanced MLOps practices and deployment strategies
• Deep learning architectures for materials science
• Cloud-based ML pipelines (AWS/Azure)

• 💼 LinkedIn
• 📧 Open to collaborations in AI for materials science and industrial applications

💡 Passionate about using AI to solve real-world problems in materials engineering and manufacturing