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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.
Here are a few 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.
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.
Neural Variational Inference For Topic Models. Akash Srivastava and Charles Sutton. In Open Review submission. 2016.
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 (very old).
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.
Some of my research projects have dedicated pages.
But not all of my research fits into one of these web sites. To get the whole story, read all of my papers!
Hobbies: I live with cats and fish, who don't interact as much as you might think. I've played a few computer games, mostly adventure games and RPGs. I play Go (圍棋, 囲碁, 바둑). If you would like to know where to play Go in person, try the American Go Association or the British Go Association. I enjoy cooking.
When I was in university, I was a bit sillier than I am now, so I created a silly web site called al.oysi.us. The URL is easy to remember, because as I'm sure you're aware, Aloysius is my middle name. Warning: May not suitable for the silliness-challenged.
Does this page seem a bit boring? That's because you haven't cracked the Easter egg yet.