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BIO: I am a Research Scientist at Google DeepMind. Before I was a Student Researcher at Google Brain in the group of Jascha Sohl-Dickstein, working particularly with him, Roman Novak, and Jeffrey Pennington, and a PhD student in Machine Learning Group at the University of Cambridge, supervised by Rich Turner and Zoubin Ghahramani. In 2022, I was a visiting scholar in Michael I. Jordan's group at UC Berkeley.
RESEARCH INTERESTS: Primarily neural network scaling (scaling laws, optimization, scaling-centric evaluation). Also interested in open-endedness, world models, and reinforcement learning. I also worked on human-AI and AI-AI interactions, optimal transport, and Bayesian machine learning.
The rest of this site was not updated since I finished my PhD in 2022 (thesis link).
I am a co-author of the original paper that proved deep NNs behave as Gaussian processes when layer widths are sufficiently large.
I am also one of the original authors of Neural Tangents, a neural net Python library which automates computation of large width NN limits, prediction of optimisation paths of wide finite networks, and much more.
Among else, this research enabled training of NNs with thousands of layers, orders of magnitude faster uncertainty estimation, and finding optimal hyperparameters for the largest current NNs using a fraction of compute needed for a single training run.
Wessel and I have worked through the excellent book High-Dimensional Statistics: A Non-Asymptotic Viewpoint by Professor Wainwright . We published our exercise solutions hoping they will help others who want to learn about modern estimation theory.
I wish more people knew about the fantastic and important work being done by the Humane League, the Albert Schweitzer Foundation, and the Animal Charity Evaluators.
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