Reader (= Associate Professor)
School of Informatics
University of Edinburgh
Faculty Fellow, The Alan Turing Institute
Office: IF 3.26
Voice (W): +44 (0) 131 651 5634
Advice for PhD Students
at Google Scholar|
Software I Use
We are currently hiring a postdoc for an exciting project on joint models for ML/NLP and computer programs!
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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 can see my publications, by topic.
Or you might be interested in these recent highlights:
Learning Continuous Semantic Representations of Symbolic Expressions. Miltiadis Allamanis, Pankajan Chanthirasegaran, Pushmeet Kohli and Charles Sutton. In Open Review submission. 2016.
A Convolutional Attention Network for Extreme Summarization of Source Code. Miltiadis Allamanis, Hao Peng and Charles Sutton. In International Conference in Machine Learning (ICML). 2016.
Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation. Akash Srivastava, James Zou, Ryan P. Adams and Charles Sutton. In Workshop on Human Interpretability in Machine Learning Workshop on Human Interpretability in Machine Learning (co-located with ICML). 2016.
Neural Variational Inference For Topic Models. Akash Srivastava and Charles Sutton. In Open Review submission. 2016.
A Subsequence Interleaving Model for Sequential Pattern Mining. Jaroslav Fowkes and Charles Sutton. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016.
Parameter-Free Probabilistic API Mining across GitHub. Jaroslav Fowkes and Charles Sutton. In Foundations of Software Engineering (FSE). 2016.
Mining idioms from source code. Miltos Allamanis and Charles Sutton. In Symposium on the Foundations of Software Engineering (FSE). 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.)
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.
I collaborate with a wonderful group of students and researchers who have, for whatever reason, chosen to go under the name CUP: Charles's Uncertain People. There is a CUP Reading Group, to which all are welcome.
A subgroup of CUP, called MAST (Machine learning for the Analysis of Source code Text), focuses on machine learning for software engineering and programming languages. Our software in this area is available via the MAST Github group.
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.
What do I have in common with a popular social bookmarking site? If this question intrigues you, then explore the web site of my non-work alter ego, al.oysi.us.
Why doesn't this page have color? Because you haven't cracked the Easter egg yet.