Friday, June 26, 2009
Congratulations to Rod Burstall
Rod has made deep, seminal contributions to the design of programming languages and the field of program verification. These contributions, which many of us now take for granted, include the introduction of algebraic datatypes coupled with pattern-matching clausal function definitions as found in Hope, ML, Haskell and Coq; the generalization and use of structural induction for proving properties of programs; the fold-unfold method for deriving efficient, provably-correct programs from easy to understand prototypes; mechanisms for reasoning about pointer-based, imperative programs that directly led to the development of separation logic; proof techniques and connections to modal logic for reasoning about concurrent programs; and the use of dependent types and algebraic specifications for constructing module systems that directly influenced SML and OCaml. Through these amazing contributions and his collaborations and mentorship, he helped build one of the most important centers of programming research at Edinburgh, which was eventually institutionalized as the Laboratory for Foundations of Computer Science.
Amin Coja-Oghlan: Best Paper Award ICALP 2009
Wednesday, June 10, 2009
I am delighted to announce the appointment of Dr. Charles Sutton to a SICSA lecturership in the School of Informatics.
Charles has been a postdoctoral researcher at the University of California, Berkeley, since 2007. His recent work has aimed at new statistical machine learning methods designed to aid the management of large-scale computer systems. In particular, he has developed methods for performance modeling that are rooted in machine learning, applying them to the control, visualization, and diagnosis of distributed Web applications. More generally, his research interests include machine learning, graphical models, approximate inference, structured prediction, natural language processing, and the application of machine learning methods to computer systems. Charles received his PhD from the University of Massachusetts Amherst in 2008. His thesis work concerned efficient training methods for conditional random fields, with applications in natural language processing.
Sunday, June 07, 2009
Iain received MA and MSci degrees in Natural Sciences (Physics) from the University of Cambridge before obtaining a PhD from the Gatsby Computational Neuroscience Unit at University College London. His thesis introduced a range of new 'Markov chain Monte Carlo' algorithms for solving integrals in hard statistical inference problems. While at Gatsby Iain also developed strong interests in probabilistic modelling and efficient algorithms for solving inference problems.
Partly supported by a Canadian Commonwealth Research Fellowship, Iain moved to Toronto in 2007 and joined the Machine Learning group there as a postdoctoral fellow. He has continued to expand the applicability of Markov chain Monte Carlo methods for statistical applications, such as the evaluation of large-scale probabilistic models. Iain has also formed collaborations to apply and extend hierarchical Bayesian methods. Recent application areas include understanding human perception and inferring celestial dynamics.
Iain will take a leave of absence to allow him to complete his Fellowship in Toronto, but he plans to make several extended visits to Edinburgh before moving to Scotland in 2010.