Jacob Daniel Moss

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:

  • Modelling gene regulation using high-throughput genomics,
  • Latent Force Models,
  • Wishart Processes,
  • Stochastic Neural ODEs,
  • Graph machine learning,
  • Uncertainty and confidence estimation.

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!

Selected Publications

Neural ODE Processes

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.
In International Conference on Learning Representations. ICLR, 2021
Also published as a workshop paper at NeurIPS workshop on Machine Learning and the Physical Sciences. NeurIPS, 2020
Paper | Code

Gene Regulatory Network Inference with Latent Force Models

Moss, J., and Liò, P. . Arxiv, 2020.

Talks

Teaching & Supervising