Past and Current PhD Students and Postdocs

(with year and thesis title for graduated students)

Francesco Vivarelli (1999)
Studies on the Generalisation of Gaussian Processes and Bayesian Neural Networks
Nicholas Adams (2001)
Dynamic Trees: A Hierachical Probabilistic Approach to Image Modelling
Stephen Felderhof (2003)
Tree-Structured Graphical Models and Image Analysis
Matthias Seeger (2003)
Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations
Michalis Titsias (2005)
Unsupervised Learning of Multiple Objects in Images
Michael Schouten (2006)
Modeling Dependencies in Genetic-marker Data and its Application to Haplotype Analysis
John Quinn (2007)
Bayesian Condition Monitoring in Neonatal Intensive Care
Moray Allan (2007)
Sprite Learning and Object Category Recognition using Invariant Features
Edwin Bonilla (2008)
Compilers that Learn to Optimise: A Probabilistic Machine Learning Approach
Kian Ming Adam Chai (2010)
Multi-task Learning with Gaussian Processes
Nicolas Heess (2011)
Learning Generative Models of Mid-level Structure in Natural Images
Ondrej Mandula (2013)
Super-resolution Methods for Fluorescence Microscopy
Ali Eslami (2013)
Generative Probabilistic Models for Object Segmentation
Jyri Kivinen (2013)
Statistical Models for Natural Scene Data
Ioan Stanculescu (2015)
Dynamical Models for Neonatal Intensive Care Monitoring
Konstantinos Georgatzis (2017)
Dynamical Probabilistic Graphical Models applied to Physiological Condition Monitoring
Pol Moreno
Lukasz Romaszko
Charlie Nash
Alex Bird
Taha Ceritli