I am a Senior Lecturer (equivalent to US Assistant Professor) in Computer Science at the University of Bristol's Computational Neuroscience Unit.
In recent years, Bayesian inference has fallen by the wayside as deep neural networks have taken precedence in Machine Learning.
We are on a mission to "Make Bayes Great Again" by showing that many of the things we do in deep learning are Bayesian (e.g. Adam) developing new Bayesian models with the power and flexibility of neural networks (deep kernel processes), showing that anything you can do with deep learning you can do with Bayes (e.g. semi-supervised learning) and showing that the "hacks" you need to make Bayesian neural networks work are actually principled (the cold-posterior effect).
Prior to my move to Bristol, I did a PostDoc with Prof. Máté Lengyel in the University of Cambridge, and a PhD with Prof. Peter Latham at the Gatsby Unit. Through that time, I have been simultaneously working on pure machine learning, theoretical neuroscience, and the analysis of experimental data ranging from calcium imaging to behaviour.
There are PhD positions available including for an October 2021 start. Deadline January 15th. Please contact me if you are interested, or consider the Interactive AI Center for Doctoral Training. I am happy to consider China Scholarship Council students.
Deep neural networks have revolutionised machine learning. But what makes deep networks so effective? We give rigorous theory, describing how the flexibility in finite (but not infinite) neural networks shapes representations to solve difficult tasks.
How should we train our neural network? There is no easy answer: many, many algorithms have been suggested, and at present, there is no easy way to choose between them. Remarkably, we can show that three of the most important techniques: Adam, decoupled weight decay, and RAdam arise by considering stochastic gradient descent as a Bayesian inference problem.
How can we perform accurate inference in large-scale models with rich statistical structure. Here, we apply insights from classical approaches such as particle filtering and message passing, to obtain exponentially many importance samples in state-of-the-art deep variational autoencoders
What do our neural networks know? And more importantly, what don't they know? Here, we apply ideas from areas ranging from neuroscience to the theory of deep networks to the problem of reasoning accurately about uncertainty in neural network parameters.
How can deep models learn about the structure of the world without explicit supervision? Here, we impose high-level, interpretable structure on the neural representations induced by state-of-the-art flow-based models of natural stimuli.
How can the brain compute efficiently under energetic constaints? And how can the brain represent uncertainty about the world? Remarkably, we have been able to show that the solution to these problems is one and the same: efficient computation automatically reasons about uncertainty, and reasoning about uncertainty allows the brain to compute efficiently.