Long-serving Informatics staff congratulated with awards from Principal

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David Aspinall studied at the University of Cambridge and then the University of Edinburgh, where he is now a senior member of academic staff in the School of Informatics. He has contributed to a substantial body of research in machine-assisted mathematics, programming and specification languages, and computer security.

Margaret Blake is a Research Student Funding Administrator at the School of Informatics, and member of the Graduate School.

Nigel Goddard worked in Computational Neuroscience during his PhD (at the University of Rochester, Caltech, Hughes Aircraft, and Carnegie Mellon University) and therafter (at the University of Pittsburgh and the University of Edinburgh) until 2003, gradually evolving his work into helping found the field of Neuroinformatics. After a few years as an entrepreneur (co-founder of Axiope, which has become ResearchSpace), he returned to academic research focussed on the key role that energy plays in the modern world using tools from machine learning.  He has been principal investigator and co-investigator on numerous research and training grants, including study of parallel simulation tools for computational neuroscience (which led to the NeuroML standard), co-founding the EPSRC Doctoral Training Centre in Neuroinformatics (2002-2017), and establishing the functional MRI facility at the University of Edinburgh. Currently he is PI and Co-I on several grants which study ways to increase the efficiency of energy use in buildings; helps to lead the EPSRC Centre for Doctoral Training in Data Science; is Director of the Institute for Adaptive and Neural Computation; collaborates on resource-focussed macroeconomic models; and continues to provide strategic advice to ResearchSpace.

John Longley studied Mathematics at the University of Cambridge, and worked for a year in Formal Methods at Plessey Research and Technology before embarking on a PhD in the Laboratory for Foundations of Computer Science, University of Edinburgh. His work lies at the intersection of mathematical logic and theoretical computer science, focusing in particular on concepts of computability in higher-order settings. Together with Dag Normann of Oslo University, he has co-authored a book, 'Higher-Order Computability', which is due to be published by Springer in 2015.

Amos Storkey is Professor of Machine Learning and Artificial Intelligence at the School of Informatics, University of Edinburgh. He is known both for his contributions to novel method development in machine learning, neural networks and AI, as well as long-standing applications of machine learning methods in a number of areas including medical imaging. He is particularly interested in the development of machine learning methods that are robust to the usual changes between training environment and deployment, and methods that go beyond simple applications of computer vision methods to medical imaging, and instead take account of the particular needs of the medical setting.

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Chris Williams is Professor of Machine Learning in the School of Informatics, University of Edinburgh. He obtained his MSc (1990) and PhD (1994) at the University of Toronto, under the supervision of Geoff Hinton. He was a member of the Neural Computing Research Group at Aston University from 1994 to 1998, and has been at the University of Edinburgh since 1998. Chris is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. This includes theoretical foundations, the development of new models and algorithms, and applications. His main areas of research are in models for understanding time-series, visual object recognition and image understanding, unsupervised learning, and Gaussian processes. At the Turing he also has interests in improving the data analytics process, looking to address the issues of data understanding and preparation that are widely quoted as taking around 80% of the time in a typical data mining project.