Ricardo Silva got his PhD at Carnegie Mellon in 2005, in the newly formed Machine Learning Department. He moved to UCL as a Senior Research Fellow in the Gatsby Computational Neuroscience Unit. After a year at the Statistical Laboratory at Cambridge as a postdoc, Ricardo returned to UCL to join the Department of Statistical Science as a Lecturer in 2008.
His research focuses on: 1. Algorithms for probabilistic inference: approximations for likelihoods and posterior distributions based mostly of variational approximations and Markov chain Monte Carlo. 2. Latent variable models: measurement error models and generalisations of probabilistic principal component analysis, as well as the modelling of network data. 3. Machine learning for causal inference: identification and discovery of models with unmeasured confounding and measurement error. He has also recently focused on applied work on human movement modelling, including partners such as TfL (for traffic data), UCL Institute of Behavioural Neuroscience (human navigation strategies) and Stratagem Ltd (sports modelling).