Statistical machine learning, graphical models, probabilistic inference. Applications in natural language processing, processing of programming languages, and probabilistic models of computer system performance. 

2008, PhD in Computer Science, University of Massachusetts Amherst.

2005, MS in Computer Science, University of Massachusetts Amherst.

1999, BA in Computer Science and Philosophy, St. Mary’s College of Maryland.

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.

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.