AI research & engineering / London
I work on post-training and machine learning systems at Meta. My background spans language models, optical computing, and the physics of non-equilibrium systems.
01 / Research
From learning algorithms to the physical systems that run them.
Optical computing
I was the AI lead on this project. The architecture combines optics and analog computation to improve efficiency for AI inference and certain optimization problems, alongside model designs that fit the machine.
Language models
Work on implicit state-space models that recover non-linear state transitions with more parallelizable training, scaled to reasoning tasks and large language-model pretraining.
Stochastic physics
Experimental work on tuned barrier shapes showing regimes where reaction rates can increase rather than decrease with barrier height, against the usual Arrhenius intuition.
Exploring rewritable holographic storage as an alternative to hard disk drives, combining material experiments, workload-aware system design, and machine learning for data recovery.
Conditional generative modeling for phase retrieval in digital holography, aimed at finding holograms that produce small laser patterns with high accuracy.
A protocol for extracting relative path probabilities from measured particle trajectories, making stochastic actions and the most probable paths across a barrier experimentally accessible.
Using first-passage-time statistics to reveal intermediate states in molecular processes and connect measured transition dynamics to the underlying energy landscape.
Experimental and theoretical work on when fundamental inversion symmetries in first-passage and transition-path dynamics hold, and how they break down on mesoscopic and molecular scales.
A line of work on non-equilibrium dynamics in biological and active-matter systems, including broken detailed balance in living systems and active filament networks.
03 / About
At Meta, I work on post-training, agentic harnesses, and systems for frontier models. Before that, I spent six years at Microsoft Research Cambridge, working across machine learning and optical computing.
My PhD at Cambridge explored stochastic thermodynamics, optical tweezers, and machine learning, focusing on how to understand and control systems shaped by fluctuations.
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