Bio

Charles Sutton is a Reader (equivalent to Associate Professor in Machine Learning at the University of Edinburgh. He has over 50 publications in a broad range of applications of probabilistic machine learning. His work in machine learning for 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.

Research interests

Charles's research focuses on developing new machine learning methods that are motivated by the demands of new, cutting-edge practical problems. He develops new techniques in probabilistic machine learning, approximate inference in graphical models, and more recently deep learning. In general, these motivating applications come from a wide range of areas, including natural language processing, analysis of computer systems, software engineering, sustainable energy, and exploratory data analysis. Most recently, he is particularly fascinated by two important application areas: machine learning and NLP methods to improve software development, and machine learning and artificial intelligence methods to support the full practical workflow of data science.