Professor Aaron Quigley new SICSA Director

Congratulations to Professor Aaron Quigley who has been appointed as the new Director of SICSA. Aaron, the Chair of Human Computer Interaction co-founded SACHI, the St Andrews Computer Human Interaction research group and served as its director from 2011-2018.

In his volunteer roles he is the ACM SIGCHI Vice President for Conferences (on the ACM SIGCHI Executive Committee), member of the ACM Europe Council Conferences Working Group, a board member of ScotlandIS and an ACM Distinguished Speaker. Aaron will be general co-chair for the ACM CHI conference in Asia in 2021.

For more information about Professor Quigley, please see

Virtual Reconstruction of Medieval Home of the Lords of the Isles

The School of Computer Science’s Open Virtual Worlds team has created a digital reconstruction of the medieval home of the Lords of the Isles at Finlaggan on Islay. The new reconstruction will form part of a virtual reality exhibit at the Finlaggan Trust Visitor Centre. A preview can be seen on Vimeo.

Today, Finlaggan seems a peaceful backwater. Yet, in the Middle Ages it was a major power base. The two islands of Eilean Mor (or Large Isle) and Eilean na Comhairle (or Council Isle) on Loch Finlaggan were once the ceremonial and political heart of the Lordship of the Isles – which covered the Hebrides and parts of mainland Scotland and Ulster.

Traditionally the Lordship was held by the MacDonald family. However, following disputes in the fifteenth century the Scottish kings sought to curtail the MacDonalds’ influence, and in the 1490s James IV sent a military expedition to sack Finlaggan. Many of the buildings at Finlaggan were destroyed at this time, and over the centuries that followed the site sank into relative obscurity.

The reconstruction by the Open Virtual Worlds team (and its spin-out company Smart History) shows Finlaggan as it may have appeared in the fifteenth century. It is based on discoveries made by the Finlaggan Archaeological Project, led by archaeologist Dr David Caldwell (formerly of the National Museum of Scotland), who provided advice to the St Andrews researchers.

The digital project was led by Dr Alan Miller of the School of Computer Science, while digital modelling was undertaken by Sarah Kennedy of the School of Computer Science, with additional historical research by Dr Bess Rhodes of the School of History and the School of Computer Science. Drone footage of the site and photogrammetry of historic artefacts were also undertaken by the project team, including work by Computer Science’s Dr CJ Davies, Dr Iain Oliver, and Catherine Anne Cassidy. A short video about the project can be viewed here.

Discover more about the Finlaggan Trust and how to visit this historic place at:

Graduation Reception: Wednesday 26th June

Event details

  • When: 26th June 2019 11:00 - 13:00
  • Where: Cole Coffee Area
  • Format: graduation

The School of Computer Science will host a graduation reception on Wednesday 26th June, in the Jack Cole building, between 11.00 and 13.00. Graduating students and their guests are invited to the School to celebrate with a glass of bubbly and a cream cake. Computer Science degrees will be conferred in an afternoon ceremony in the Younger Hall. Family and friends who can’t make it on the day can watch a live broadcast of graduation. Graduation receptions have been held in the school from 2010.

A class photo will be taken at 12.00 outside the Jack Cole building.

Alex Bain completes 2019 London Marathon

Congratulations to School Manager Alex Bain, who completed the London Marathon for the fourth time on Sunday, raising funds for Guide Dogs. Alex, runner no 33950 is pictured below with his finisher’s medal. Donations to recognise his achievement and the training involved, can be made via his Justgiving page. A charity bake sale in the School of Computer Science earlier this month helped to raise just over £520.

Juho Rousu: Predicting Drug Interactions with Kernel Methods

Event details

  • When: 30th April 2019 14:00 - 15:00
  • Where: Cole 1.33a
  • Format: Seminar

Predicting Drug Interactions with Kernel Methods

Many real world prediction problems can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs. We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.

Anna Cichonska, Tapio Pahikkala, Sandor Szedmak, Heli Julkunen, Antti Airola, Markus Heinonen, Tero Aittokallio, Juho Rousu; Learning with multiple pairwise kernels for drug bioactivity prediction, Bioinformatics, Volume 34, Issue 13, 1 July 2018, Pages i509–i518,

Short Bio:
Juho Rousu is a Professor of Computer Science at Aalto University, Finland. Rousu obtained his PhD in 2001 form University of Helsinki, while working at VTT Technical Centre of Finland. In 2003-2005 he was a Marie Curie Fellow at Royal Holloway University of London. In 2005-2011 he held Lecturer and Professor positions at University of Helsinki, before moving to Aalto University in 2012 where he leads a research group on Kernel Methods, Pattern Analysis and Computational Metabolomics (KEPACO). Rousu’s main research interest is in learning with multiple and structured targets, multiple views and ensembles, with methodological emphasis in regularised learning, kernels and sparsity, as well as efficient convex/non-convex optimisation methods. His applications of interest include metabolomics, biomedicine, pharmacology and synthetic biology.

PhD viva success: Evan Brown

Congratulations to Evan Brown, who successfully defended his thesis today. He is pictured with Internal examiner Dr Tristan Henderson and external examiner Professor Chris Marsden, Professor of Internet Law at the University of Sussex.

Evan’s PhD research on using corpus linguistics to build collaborative legal research tools was supervised by Professor Aaron Quigley.

Continued success for MSc student Jessica Cooper

The work of our MSc student, Jessica Cooper, supervised by Oggie Arandjelovic on the use of deep learning for the analysis of ancient Roman coins has been attracting widespread attention. From tech media to web sites of history, heritage, and numismatics focused communities, Jessica’s work has been recognized as highly innovative, with a potential to change the direction of research in the area. Jessica will be rejoining St Andrews in a month’s time, working with Oggie Arandjelovic on deep learning in pathology image analysis.

Best paper finalist award for Xingzhi Yue and Neofytos Dimitriou

A paper describing the work of our MSc student Xingzhi Yue and PhD student Neofytos Dimitriou, supervised by Oggie Arandjelovic and in collaboration with the School of Medicine, gets the best paper finalist award at the latest International Conference on Bioinformatics and Computational Biology (BICOB 2019). The key contribution of the work is a novel deep learning based algorithm for the analysis of extremely large pathology image slides, capable of automating and improving colorectal cancer prognosis.