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!
 Norcliffe, A., Bodnar, C., Day, B., Moss, J., & Liò, P. Neural ODE Processes. In International Conference on Learning Representations. ICLR, 2021
 Norcliffe, A., Bodnar, C., Day, B., Moss, J., & Liò, P. Neural ODE Processes. In NeurIPS workshop on Machine Learning and the Physical Sciences. NeurIPS, 2020.
 Moss, J., and Liò, P. Gene Regulatory Network Inference with Latent Force Models. Arxiv, 2020.