About Me
Passionate about leveraging data to drive positive change, particularly in tech-for-good sectors such as democracy, AI safety (particularly (mis-)alignment and training dynamics), energy, and environmental sciences. Pioneered the first algorithm for efficient Approximate Manifold Sampling and contributed to the development of Integrator Snippets, with a focus on Simulator-Based Inference (SBI). Some random things I have organised: Generative Models Reading Group, Neural Network Reading Group, Bristol Online Charity Danceathon, Postgraduates in AI (PAI) Link. Below, you can find my course on LLMs.
Other things I am passionate about include:
- Basketball 🏀
- Dancing (mostly Modern Jive, West Coast Swing and Blues) 💃
- Journalism, especially investigative (e.g. Bellingcat) 🕵
- Science of Cooking 🧪 🥘
- Exercising whether bodyweight, calisthenics or at the gym 🏋️
Interests
- AI Safety, Trustworthy AI
- Score-based sampling (HMC, MALA)
- Generative Models (LLMs, DDGM)
- Approximate Manifold Sampling
- Scalable/Robust inference (SMC,SBI)
Education
PhD in Statistical Machine Learning, 2020-2023
University of Bristol
BSc in Mathematics with Year in Employment, 2019
University of Southampton
