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.
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.”
Research carried out by Charlie Blake and Ian Gent to compute the approximate odds of winning any version of solitaire features in Major Nelson’s Video Podcast [Interview with Ian and Charlie starts 23:56] for XBox news today.
Today is National Solitaire Day and the 30th anniversary of the game. The celebrations include an invitation to participate in a record breaking attempt at the most games of Microsoft Solitaire completed in one day. You can download the collection free or play it through your browser.
The Klondike Solitaire research also featured in the New Scientist last year.
Link to the full paper on arxiv: https://arxiv.org/abs/1906.12314
Online article published in Technology Nov 17th 2019: https://www.newscientist.com/article/2223643-we-finally-know-the-odds-of-winning-a-game-of-solitaire/
Congratulations to Head of School Simon Dobson who has been elected to the Royal Society of Edinburgh for his exceptional achievements in science. This prestigious award recognises expertise which supports the “advancement of learning and knowledge in Scottish public life”. The RSE established in 1783, plays a leading role in “the development of a modern enlightenment that will enable Scotland to contribute significantly to addressing the global challenges facing humanity in the 21st Century”. The RSE announced its newly-elected 2020 Fellows on Tuesday, describing Fellows as “leading thinkers and experts from Scotland and around the world whose work has a significant impact on our nation”.
Simon works on adaptive systems, especially those driven by sensors. He has concentrated recently on how to make robust decisions from sensor data as the sensor system degrades, which is a critical foundation for making best use of the torrent of data coming from the “Internet of Things”. He is also interested in complex processes such as how epidemics spread in a population and how urban transport networks function, where mathematical models need to be complemented by repeatable and validated computational experiments that pose a major software challenge.
This Week Dr Juliana Bowles brought together nine leading academic and industry partners for the 4th Consortium meeting for the Serums project.
The project aims to produce tools and technologies to support future-generation healthcare systems that will integrate home-based healthcare into a holistic treatment plan, reducing cost and travel-associated risks and increasing quality of healthcare provision.
For further information on the project visit the Serums website
Image and text provided by Annemarie Paton
The medical school is holding a Seminar on Wednesday 8th January @1400-1530
New tools and methods to prevent blindness.
Seminar room 1, Medical and Biological Sciences Building
- Dr. Andrew Blaikie, St Andrews
- Dr. Craig Robertson, CEO Epipole Ltd
Hand held fundus cameras
- Prof Congdon, Queen’s University Belfast
Overview of Global Ophthalmology
- When: 8th January 2020 14:00 - 15:30
- Format: Seminar
The Melville Trust for the Care and Cure of Cancer have funded a PGR Studentship relative to the project entitled ‘Detecting high-risk smokers in Primary Care Electronic Health Records: An automatic classification, data extraction and predictive modelling approach’.
Next Monday is the annual St Andrews Bioinformatics workshop in Seminar Room 1, School of Medicine. Some of the presentations are very relevant to Computer Science, and all should be interesting. More information below:
14:00 – 14:15: Valeria Montano: The PreNeolithic evolutionary history of human genetic resistance to Plasmodium falciparum
14:15 – 14:30: Chloe Hequet: Estimation of Polygenic Risk with Machine Learning
14:30 – 14:45: Roopam Gupta: Label-free optical hemogram of granulocytes enhanced by artificial neural networks
15:00 – 15:15: Damilola Oresegun: Nanopore: Research; then, now and the future
15:15 – 15:30: Xiao Zhang: Functional and population genomics of extremely rapid evolution in Hawaiian crickets
15:30 – 16:00: Networking with refreshments
16:00 – 17:00: Chris Ponting: The power of One: Single variants, single factors, single cells
You can register your interest in attending here.
- When: 10th June 2019 14:00 - 17:00
- Format: Lecture, Talk, Workshop
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 https://aaronquigley.org.
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, https://doi.org/10.1093/bioinformatics/bty277
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.
- When: 30th April 2019 14:00 - 15:00
- Where: Cole 1.33a
- Format: Seminar