I'm a PhD student at Clare College, Cambridge University, co-supervised by Rich Turner at the Machine Learning Group and John Aston at Statslab. My research is broadly into machine learning and computational statistics. Before starting my PhD, I studied for a BA and MMath at Cambridge. I focused mainly on Probability and Algebra courses in the final year, and wrote my Part III essay "Mixing Times of Random Transpositions" under the supervision of Nathanaël Berestycki. I worked as an actuarial consultant before starting my PhD, and during my PhD I've interned as a Quantitative Researcher at G-Research, and as a Research Scientist at DeepMind.
I'm interested in many areas across machine learning, statistics, probability, and optimisation, and interactions with areas of pure maths such as group theory and optimal transport theory. To date, I've worked on areas including MAP inference for discrete graphical models, Monte Carlo methods (particularly over non-Abelian groups), reinforcement learning, and probabilistic deep learning.
We recently arxived an extended version of our ICLR paper Gaussian Process Behaviour in Wide Deep Neural Networks. In this newer version of the paper, we formally show that wide stochastic deep networks converge to Gaussian processes under much milder conditions than in the ICLR version.
Our paper Structured Evolution with Compact Architectures for Scalable Policy Optimisation has been accepted to ICML 2018.Gaussian Process Behaviour in Wide Deep Neural Networks
I'm currently giving supervisions for Part IB Statistics, and in previous years have given supervisions on Part IA Groups, Part IA Probability, Part IB Linear Algebra, and Part IB Statistics. I gave examples classes for Part III Bayesian Modelling and Computation in Lent 2017, and run MATLAB introduction sessions for Maths Tripos students.
Analysing Distributional Reinforcement Learning
Microsoft Research Cambridge, February 2018
Optimal Transport for Machine Learning (with Wenbo Gong)
Cambridge MLG Reading Group, November 2017
MAP Inference in Undirected Graphical Models
Cambridge MLG Research Talk, November 2016
Tightness of LP Relaxations for Almost Balanced Models
International Conference on Principles and Practice of Constraint Programming (CP), Toulouse, September 2016
Coupling from the Past
Machine Learning Group Tea Talk, March 2016
Stochastic Approximation Theory (with Yingzhen Li)
Cambridge MLG Reading Group, November 2015
Feel free to get in touch via mr504 [ at ] cam [ dot ] ac [ dot ] uk.My LinkedIn profile