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

[3] Norcliffe, A., Bodnar, C., Day, B., Moss, J., & Liò, P. Neural ODE Processes. In International Conference on Learning Representations. ICLR, 2021

[2] 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.

[1] Moss, J., and Liò, P. Gene Regulatory Network Inference with Latent Force Models. Arxiv, 2020.