Computational Aspects of Deep Gaussian Processes in Data Science

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My project's area of research is the investigation of continuous-time/space models in data science. In particular, I study deep Gaussian processes. Applications for this modelling tool are abundant, thanks to its capacity of representing multi-scale behaviour. Examples where this property is of great importance include climate modelling and image analysis. Such applications give rise to the necessity of solving "standard" machine learning tasks such as regression and inference problems that are based on (deep) Gaussian processes. As of today, sampling from such models is computationally very expensive. My goal in this project is thus to improve existing sampling techniques and to develop new strategies for generating samples in a more efficient way. At a later stage, I would like to employ deep Gaussian process priors in Bayesian inference. To this end, I study efficient Markov chain Monte Carlo methods.