Science → Data → Models → Software
I'm a Chemistry graduate with a growing obsession for turning scientific problems into software.
My path started in chemistry and laboratory work, where I learned to think in terms of measurements, data, systems, and why things behave the way they do. Somewhere along the way, I started asking a different question:
"Could I make a computer do this?"
That question pulled me into Python, machine learning, deep learning, and scientific computing.
These days, I enjoy building things at the intersection of science and AI — from molecular property prediction with graph neural networks to computer-vision tools for industrial inspection.
I'm still learning a lot, but that's kind of the point. Build something → break it → figure out why → make it better.
That's what I enjoy doing.
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Graph Neural Network for molecular property prediction Predicting molecular properties from molecular structures using graph-based representations and the QM9 dataset. Python · PyTorch · GNN · Molecular ML |
Computer Vision for Friction Stir Welding AI-assisted inspection software for extracting geometric measurements from industrial welding images. Python · PyTorch · ResNet · Computer Vision |
A personal lab for experiments, projects, and ideas at the intersection of science, machine learning, and software engineering.
Build → Experiment → Measure → Improve
💬 Ask me about: Python, ML experiments, scientific computing, or the projects I'm currently breaking.
⚡ Fun fact: My favorite projects usually start with "I wonder if I can automate this."
I'm going deeper into modern AI — from neural network architectures to building LLM-powered systems that can retrieve information, use tools, and operate through structured workflows.
GNNs · Transformers · Attention · RNNs · LSTMs · GRUs
NLP · Embeddings · Semantic Search · RAG · Hugging Face
LangChain · LangGraph · Tool Calling · Structured Outputs
AI Agents · Memory · Planning · Tool Use · Agent Orchestration