Congratulations to Janis Wong, supervised by Tristan Henderson (CS) and Kirstie Ball (Management), who has passed her PhD viva subject to minor corrections.
Thanks to Alison Powell (LSE) for serving as the external examiner.
School of Computer Science
REF Results
We’re delighted that the world-leading quality of our research has been recognised in the 2021 Research Excellence Framework.
Congratulations to all members of our School!
More Information on CS Ref results can be found on our Research page
Congratulations David Letham!
Congratulations to David Letham who has been invited to the Gives Back Awards 2022, on behalf of University of St Andrews Charities Campaign.
David is one of the first winners of the making a difference Award. This new Award comes from students and staff who would like to nominate members of staff who have gone the extra mile within St Andrew’s community and beyond. Whether that be stepping up to help others during exceptional times, showing initiative or making a positive impact on the individuals/communities they have worked with
Enjoy the Ceremony!
Research participants from further education wanted
We are looking to speak to further education students of all disciplines.
Photo by David Kennedy on Unsplash
We want to understand what students want to know about personal cyber security and how they want to learn it. To participate, you must be 16 or over and based at college, not at school, and willing to take part in an interview about this. Sessions will last a maximum of 30-40 minutes, held on Microsoft Teams. Participants will be offered a £8 voucher for their time and contributions.
If you are interested, please get in contact using the details below. You will then be given a Participant Information Sheet with further details of our research and have the opportunity to ask questions, before being asked whether you consent to participate.
Contact Details
Amy Hunt – student-cyber-awareness@st-andrews.ac.uk
This study is being conducted as part of a research study in the School of Computer Science at the University of St Andrews. The researchers are Dr Jean Carletta, Kevin Doherty, Amy Hunt, and Molly Wilson.
Only One day to go until SISCO!
SISCO Conference, 5 & 6 February
Ian Gent, Chris Jefferson and Simon Dobson are all presenting at the SISCO conference this weekend. There will be social and networking events which include free food in the medicine cafeteria. We think that these will be nice opportunities for all students, speakers and staff to get to know one other.
You are all invited to these events, both on Saturday and Sunday.
You can get your free tickets to attend and further information on their Facebook page
Welcoming Prof. Giovanna Di Marzo Serugendo for our DLS on Tuesday 9 November
As part of the schools Distinguished Lecture Series we look forward to welcoming Prof. Giovanna Di Marzo Serugendo on Tuesday 9 November.
Prof. Giovanna Di Marzo Serugendo received her Ph.D. in Software Engineering from the Swiss Federal Institute of Technology in Lausanne (EPFL) in 1999. After spending two years at CERN (the European Center for Nuclear Research) and 5 years in the UK as Lecturer, she became full professor at the University of Geneva in 2010. Since 2016, she is the Director of the Computer Science Center of the University of Geneva, Switzerland. She has been nominated in 2018 among the 100 digital shapers in Switzerland. Her research interests relate to the engineering of decentralised software with self-organising and emergent behaviour. This involves studying natural systems, designing and developing artificial collective systems, and verifying reliability and trustworthiness of those systems. Giovanna co-founded the IEEE International Conference on Self-Adaptive and Self-Organising Systems (SASO) and the ACM Transactions on Autonomous Adaptive Systems (TAAS), for which she served as EiC from 2005 to 2011.
This event will be held on Teams with further details to follow.
ACL 2021 Test of Time Award
Dr. Jean Carletta has been awarded the 2021 Association for Computational Linguistics 25-year Test-of-Time paper award for Assessing Agreement on Classification Tasks: The Kappa Statistic, Computational Linguistics 22 (1), 1996. In this paper she intervened to correct a common but misleading statistical practice. As a result, her field began to require assessments of how variability in subjective analyses could bias the claims made for their results. Although her work was based on existing content analysis best practice in the humanities, the clarity of her expression led to the paper being used widely in teaching research methods to medical students as far afield as Beijing.
Jean is a Senior Research Fellow in the School of Computer Science. She is engaged in a wide portfolio of work that takes a systems level approach to improving the impact Scotland’s academic community has on national cyber security and resilience.
https://www.aclweb.org/portal/content/announcement-2021-acl-test-time-paper-award-0
Beautiful Image captured by Ryo Yanagida featured on BBC weekly “Your photo of Scotland”.
Seminar – Richard Connor – 5th November
The second school seminar on 5th November at 2pm, on Teams. If you do not have the Teams link available please contact the organiser, Ian Gent.
Dimensionality Reduction in non-Euclidean Spaces
Richard Connor
Deep Learning (ie Convolutional Neural Networks) gives astoundingly good classification over many domains, notably images. Less well known, but perhaps more exciting, are similarity models that can be applied to their inner layers, where there lurk data representations that can give a much more generic notion of similarity. The problem is that these data representations are huge, and so searching a very large space for similar objects is inherently intractable.
If we treat the data as high-dimensional vectors in Euclidean space, then a wealth of approximation techniques is available, most notably dimensionality reduction which can give much smaller forms of the data within acceptable error bounds. However, this data is not inherently a Euclidean space, and there are better ways of measuring similarity using more sophisticated metrics.
The problem now is that existing dimensionality reduction techniques perform analysis over the coordinate space to achieve the size reduction. The more sophisticated metrics give only relative distances and are not amenable to analysis of the coordinates. In this talk, we show a novel technique which uses only the distances among whole objects to achieve a mapping into a low dimensional Euclidean space. As well as being applicable to non-Euclidean metrics, its performance over Euclidean spaces themselves is also interesting.
This is work in progress; anyone interested is more than welcome to collaborate!
Learning to Describe: A New Approach to Computer Vision Based Ancient Coin Analysis
The work on deep learning based understanding of ancient coins by Jessica Cooper, who is a Research Assistant and a part-time PhD student supervised by Oggie Arandjelovic and David Harrison has been chosen as a featured, “title story” article by the Journal Sci where it was published in a Special Issue Machine Learning and Vision for Cultural Heritage.