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I'm a machine learning scientist at BlueCrest capital management, where I lead the efforts on designing strategies based on quantitative ML and generative models.
Previously, I was at Amazon, building Bayesian emulators for Amazon's marketplace simulator, as an applied machine learning scientist. Before Amazon, I worked as an applied scientist at Secondmind, specialising in probabilistic models for time series analysis and forecasting. I received my PhD in Computing from Imperial College London.
I actively do research around Gaussian processes (GPs) with particular focus on fast and efficient approximate inference. I am also interested in state-space models, graph models, stochastic variance estimation (heteroskedasticity) and optimising GPs with natural gradients.