Dimitris is a Research Associate in the Data Science Department at EURECOM. The subject of his Ph.D. thesis has been on approximating the stochastic behaviour of Markovian process algebra models. He has worked on the application of machine learning and formal methods for the analysis of stochastic dynamical systems. His current research interests revolve around Bayesian inference methodologies for machine learning, including Gaussian processes and Bayesian neural networks.