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

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.

Zoë Nengite awarded Principal’s Medal

Congratulations to Zoë Nengite who has been awarded The Principal’s Medal in recognition of outstanding academic achievement and exceptional activities within the University and the wider St Andrews community. The Medal is awarded to students who have both excellent academic accomplishments and those who have inspired and supported their peers and who have often undertaken extensive advocacy work, which has improved life for many of their fellow students.

Zoë sent us a reflection on time spent studying in the School and a photo celebrating with Mum.

“I’m really sad that my time at St Andrews has come to an end. I will especially miss the School of Computer Science. We are such a close community of students and staff alike. I will even miss the Jack Cole labs, despite spending many hours with my head in my hands stuck on a problem gripping my mug of coffee. I always knew that help wasn’t too hard to find.

“Some of my best memories are from my time at St Andrews. Most of them spent with my closest friends who also studied Computer Science. Coming from London, I was apprehensive about St Andrews, but it quickly became a place I called home. I think even years from now, it will always be somewhere I call home.”

The award was announced during the virtual conferral of degrees in July. Zoë hopes to attend a rescheduled Class of 2020 Ceremony in the future where we look forward to celebrating with her in person.

Leverhulme Early Career Fellowship for Nguyen Dang

Congratulations to Dr Nguyen Dang, who has been awarded a Leverhulme Trust Early Career Fellowship. The 3 year Fellowships are intended to assist those at an early stage of their academic careers to undertake a significant piece of publishable work. Nguyen will be researching Constraint-based automated generation of synthetic benchmark instances.

Abstract summary: “Combinatorial problems such as routing or timetabling are ubiquitous in society, industry, and academia. In the quest to develop algorithms to solve these problems effectively, we need benchmark instances. An instance is an example of the problems at hand for testing how well an algorithm performs. Having rich benchmarks of instances is essential for algorithm developers to gain understanding about the strengths and weaknesses of their approaches, and ensure successful applications in practice. This fellowship will provide a fully automated system for generating valid and useful synthetic benchmark instances based on a constraint modelling pipeline that supports several algorithmic techniques.”