Reader (= Associate Professor)
School of Informatics
University of Edinburgh
Office: IF 3.26
Voice (W): +44 (0) 131 651 5634
Advice for PhD Students
at Google Scholar|
Software I Use
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My research concerns a broad range of applications of probabilistic methods for machine learning, including software engineering, natural language processing, computer security, queueing theory, and sustainable energy. Although these applications are disparate, they are connected by an underlying statistical methodology in probabilistic modelling and techniques for approximate inference in graphical models.
My research strategy is based on the idea that sufficiently difficult applications motive the development of new methodology. I aim to develop new machine learning methods based on this interplay of theory and practice.
My position is funded through the Scottish Informatics and Computer Science Alliance.
My full list of publications is available. Or you might be interested in these recent highlights:
Suggesting Accurate Method and Class Names. Miltiadis Allamanis, Earl T. Barr, Christian Bird and Charles Sutton. In Foundations of Software Engineering (FSE). 2015.
Scheduled Denoising Autoencoders. Krzysztof Geras and Charles Sutton. In International Conference on Representation Learning (ICLR). 2015.
Mining idioms from source code. Miltos Allamanis and Charles Sutton. In Symposium on the Foundations of Software Engineering (FSE). 2014.
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models. Yichuan Zhang and Charles Sutton. In Advances in Neural Information Processing Systems (NIPS). 2014.
Learning Natural Coding Conventions. Miltiadis Allamanis, Earl T Barr, Christian Bird and Charles Sutton. In Symposium on the Foundations of Software Engineering (FSE). 2014.
(Winner, ACM SIGSOFT Distinguished Paper Award.)
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation. Mingjun Zhong, Nigel Goddard and Charles Sutton. In Advances in Neural Information Processing Systems (NIPS). 2014.
An Introduction to Conditional Random Fields. Charles Sutton and Andrew McCallum. Foundations and Trends in Machine Learning 4 (4). 2012.
Finally, I have a collection of brief, tutorial-style research notes.
Here are some software, Web apps, and iDevice apps that I enjoy using.
Also, I have a list of random software tips I have collected.