Some of our Compass graduates are now pursuing careers in industry, or have taken up academic positions. You can find out more about them below.
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Dr Alessio Zakaria Thesis: Asymptotic Analysis of an Adaptive Stochastic Gradient Descent Non-convexity and Markovian Dynamics – supervised by Vladislav Tadic and Christophe Andrieu
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Dr Alexander Modell Thesis: Spectral embedding of large graphs and dynamic networks – supervised by Patrick Rubin-Delanchy
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Dr Anthony Stephenson Thesis: Fast Gaussian Process Regression at Extreme Scale – supervised by Robert Allison and IBM
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Dr Conor Crilly Thesis: Uncertainty Quantification for Computer Experiments – supervised by Oliver Johnson
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Dr Dan Ward Thesis: Neural Methods for Practical Scientific Bayesian Inference – supervised by Matteo Fasiolo and Mark Beaumont
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Dr Danny Williams Thesis: Using Score-based Methods for Unnormalisable Probability Density Estimation: Truncated Density Estimation and Parameter Derivative Estimation – supervised by Song Liu
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Dr Dominic Owens Thesis: Data Segmentation and High Dimensional Time Series Analysis – supervised by Haeran Cho and CheckRisk
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Dr Euan Enticott Thesis: Structured Additive Stacking models with application in the energy domain – supervised by Matteo Fasiolo and Nick Whiteley
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Dr Jack Simons Research Scientist, InstaDeepThesis: Simulation-based Inference with Modern Generative Methods – supervised by Song Liu and Mark Beaumont
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Dr Jake Spiteri Thesis: Nonparametric Density Estimation with Kernel Mean Embeddings – supervised by Anthony Lee and Mathieu Gerber
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Dr Mauro Camara Escudero Thesis: Approximate Manifold Sampling: Robust Bayesian Inference for Machine Learning – supervised by Christophe Andrieu and Mark Beaumont
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Dr Michael Whitehouse Thesis: Fast and Consistent Inference in Compartmental Models (Introducing Poisson Approximate Likelihood Methods) – supervised by Nick Whiteley
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Dr Sam Stockman Thesis: Enhancing Earthquake Forecasting: Machine Learning Applications in Point Process Models – supervised by Maximillian Werner and Dan Lawson
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