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)
- 📧 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