- 2026
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Deciding how much to hedge, and shaping forward curves.
- 2025
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Momentum strategies, turnover control and honest backtesting.
- 2024
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Running AI on phones and edge devices: compilation and quantisation.
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Fast joint analyses with normalising flows.
- 2023
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Changing one word in a caption to edit an image with a diffusion model.
- 2021
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A Gaussian Process emulator for the power spectrum and its gradients.
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Compressing weak lensing data with MOPED and emulating it with GPs.
- 2020
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Curated courses and lectures on machine learning and deep learning.
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Highlights from ICLR, ICML, MLSS, OxML and GPSS, all online in 2020.
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The steps I follow to take a research idea to a first-author paper.
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Likelihood emulation with Gaussian Processes and Bayesian optimisation.
- 2019
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Andrew Ng at Imperial on narrow AI, small data, jobs and education.
- 2018
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Bayesian and machine learning methods for the next decade of cosmology.
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Talks and tutorials from the Data Science Summer School in Paris.
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Ten intensive days of machine learning, from k-NN to GANs.
- 2017
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Evidence for a Gaussian linear model, plus the Savage-Dickey ratio.
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Bayesian Model Selection 2 min
Bayesian evidence, Bayes factors and Occam's razor in model selection.
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Bayesian School 2016 3 min
Highlights from the 2016 Bayesian School: KL divergence and BHMs.
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Maximum likelihood fitting, applied to an A-Level physics experiment.
- 2016
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Three ways to sample any distribution, from SciPy to interpolation.
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The Gibbs sampler, with a worked Bayesian regression example in Python.
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Doing science directly on radio visibilities with Bayesian inference.
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Lessons from PyConZA on culture, flexibility and growing communities.
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Why hands-on JEDI workshops achieve more than conventional conferences.
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How Gaussian Processes do regression, with and without noise.
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What X-ray cavities in galaxy clusters reveal about AGN feedback.
Blog
Writing on research, mathematics, machine learning and statistics.