AI Researcher & Engineer | PhD in Machine Learning | Building efficient, creative AI systems
Currently: Cofounder & Research Lead at Axiotic AI | Principal Research Partner at Pieces
I build AI systems that are efficient, adaptive, and grounded in fundamentals—not just scaling for the sake of scaling.
Research Focus:
- Efficient Learning — Small models that punch above their weight
- Multimodal Intelligence — Text, images, audio, video working together
- Nature-Inspired Methods — Evolution, biology, and information theory as design principles
- On-Device AI — Models that run fast on CPUs, GPUs, NPUs
- 7th Place Worldwide at the LeRobot Global Hackathon (also organized the Edinburgh node)
- Co-organized the International Workshop on Efficient Generative AI 2024
- Published at NeurIPS 2024, ICML 2023, ICLR 2023
| Project | Description |
|---|---|
| GATE | Multi-domain benchmark suite for robust evaluation |
| TALI | Large-scale multimodal dataset (text, audio, language, images) |
| MAML++ | Meta-learning framework (Top-3 UK Open Source Awards) |
| kubejobs | Simplified Kubernetes job management for ML |
- PhD in Machine Learning — University of Edinburgh (Meta-Learning & Few-Shot Learning)
- Research Intern — Google (Few-Shot Learning)
- Speech Scientist Intern — Amazon (Alexa/Echo)
- Thousands of citations across published work
