I implement concise, reproducible code and notebooks that accompany ML experiments. The emphasis is on clear baselines, reproducibility, and compact example scripts you can run locally.
Research Interests
- Graph Representation Learning (GNN Architectures, Expressivity)
- Over-Squashing And Over-Smoothing In Graph Neural Networks
- Continual Deep Learning
- Continual Reinforcement Learning
- World Models And Model-Based Approaches
- Model-Free Reinforcement Learning And Sample Efficiency
Research work — coming soon
I'm preparing research code, notes, and experiments. Check the research page for updates and drafts.