Charles Sutton

I am hiring a postdoc to work on an exciting project: Statistical Natural Language Processing for Programming Languages.

Me and My Research

Computer systems not only process more data than ever before, but are also a source of data, in particular, data that concern their own operation. My research aims at new statistical machine learning methods designed to handle data about the operation and performance of large-scale computer systems. The ultimate goal is to improve techniques for developing, managing, and debugging computer systems. I believe that this is a rich source of applications that make fundamentally new demands on learning algorithms, encouraging the development of new machine learning methods.

Also, I maintain an interest in natural language processing, including structured prediction methods, conditional random fields, and graphical modeling approches to NLP.

Although these applications are disparate, they are connected by an underlying statistical methodology. I tend toward approaches based on probabilistic models whose parameters or structure can be estimated from data, often relying on techniques for approximate inference in graphical models.

I am part of a large machine learning group at Edinburgh. Here is some information for prospective students in the group.

My position is funded through the Scottish Informatics and Computer Science Alliance.

Recent Publications

My full list of publications is available. Or you might be interested in these recent highlights:

Finally, I have a collection of brief, tutorial-style research notes.

News

We have released a software package for creating Word Storms, coordinated groups of word clouds for visualizing corpora.

I received a Microsoft Research PhD scholarship to fund a student to work on Statistical Natural Language Processing for Programming Language Text. (2012-2015).

I have received an EPSRC grant on "Fast, Locally Adaptive Inference for Graphical Models" (2011-2014).

ICML 2012 was held in Edinburgh from 26 June-1 July 2012. I will served as local arrangements chair. ICML was collocated with COLT 2012.

Advisors, Mentors, Collaborators

My graduate advisor was Andrew McCallum at the University of Massachusetts Amherst.

I did a postdoc at the University of California, Berkeley working with Michael I. Jordan. I also collaborated with Dave Patterson, Randy Katz, Armando Fox, and Anthony Joseph in networking and systems. I participated in the RAD Lab, which focuses on issues in the design and management of data center applications.

I worked as a intern at Microsoft Research with Tom Minka.

Other collaborators include Max Welling (UCI), Chris Pal (Rochester), Khashayar Rohanimanesh (UMass), Yanlei Diao (UMass), Prashant Shenoy (UMass), Hanna Wallach (UMass), Peter Bodik (graduate student; Berkeley), Rob Hall (graduate student; CMU), Michael Sindelar (graduate student; UMass).

Software I Write

I have contributed to MALLET, a Java package for machine learning on text. MALLET includes implementations of several classification algorithms, conditional random fields, and latent Dirichlet allocation.

I have also written GRMM, a Java toolkit for inference in graphical models with discrete variables. It includes facilities for easily constructing and training CRFs with arbitrary graphical structure. I have used it to produce results for several of my papers, including those on DCRFs and skip-chain CRFs.

Charles Sutton

Me and My Research

Recent Publications

News

Students

Administration

Courses

Advisors, Mentors, Collaborators

Software I Write

Software I Use

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