Dr Kevin Han Huang
I am a postdoctoral research fellow funded by the EPSRC ProbAI Hub, working with Gareth Roberts at Warwick statistics and Boris Hanin at Princeton Operations Research & Financial Engineering. I received my PhD in machine learning from the Gatsby Computational Neuroscience Unit at UCL, where I was advised by Peter Orbanz at Gatsby and Morgane Austern at Harvard statistics. I was a visiting researcher at the LIPS group at Princeton Computer Science advised by Ryan P. Adams during Spring 2024, where I worked on AI-for-physics algorithms. Prior to my PhD, I received my Bachelor and Master in mathematics from University of Cambridge.
For the academic year 25-26, I am co-organising the ProbAI online seminar. I am also organising the ProbAI Theory of Scaling Laws Workshop at Warwick in summer 2026; stay tuned for more info.
URSS projects 2026
I am open to supervising a URSS project on the following topic:
- ML models for tabular data with applications to AI safety.
For enquiry, please email me with a CV and transcript by 15 Jan 2026.
I am a machine learning (ML) theorist, with increasingly frequent excursions to the applied side of things. On the theory end, I study the emergence of universal structures in large-scale stochastic systems. I develop and apply tools from:
- Probability theory, e.g. universality and random matrix theory;
- High-dimensional statistics, especially for non-linear estimators and dependent data;
- Symmetry-based inference;
- Stochastic optimisation and sampling theory.
On the empirical side of things, I am excited about problems that involve scaling and statistical diagnostics of ML models. Some examples include:
- Scaling laws of neural networks;
- Algorithm design for AI for materials science;
- Robustness and safety of AI models.