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Charles Sutton is a Reader (equivalent to Associate Professor: http://bit.ly/1W9UhqT) in Machine Learning at the University of Edinburgh. He is interested in a broad range of applications of probabilistic machine learning, including NLP, analysis of computer systems, software engineering, sustainable energy, and exploratory data analysis. His work in software engineering has won an ACM Distinguished Paper Award. His PhD is from the University of Massachusetts Amherst, and he has done postdoctoral work at the University of California Berkeley. He is currently Director of the EPSRC Centre for Doctoral Training in Data Science at the University of Edinburgh, and a Faculty Fellow of the Alan Turing Institute.
|POPL 2019||Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic|
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