I'm a PhD student in Machine Learning at the University of Cambridge Computer Lab, where I am supervised by Prof. Pietro Lió. My research focuses on:
I am also working on an Introduction to Probabilistic Machine Learning booklet, which is targeted at people with a background in computer science. It covers topics like MAP, Gaussian Processes, MCMC, Variational Inference, Stochastic Calculus, and more. It is updated regularly.
Previously, I did a Master's in Advanced Computer Science at the University of Cambridge. Further to my academic experience, I have worked on a number of industry projects, such as a non-invasive clinically certified heart rate monitor wristband, which won Innovator of the Year at the 2018 Future Health Summit, and an auction house asset-price prediction system for a quantitative trading firm.
Please feel free to contact me if you would like to collaborate on any of the research areas listed above!
We introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs. By maintaining an adaptive data-dependent distribution over the underlying ODE, we show that our model can successfully capture the dynamics of low-dimensional systems from just a few data-points. At the same time, we demonstrate that NDPs scale up to challenging high-dimensional time-series with unknown latent dynamics such as rotating MNIST digits.Norcliffe, A., Bodnar, C., Day, B., Moss, J., & Liò, P.
Moss, J., and Liò, P. . Arxiv, 2020.