Registration is open for the next Software Carpentry workshop in St Andrews on September 23-24 in the Parliament Hall. We will teach UNIX shell, version control with Git and programming with Python. Please see the workshop page for further details and the link to registration via PDMS.
Can a user write a good MIP model without understanding linearization? Modelling languages such as AMPL and AIMMS are being extended to support more features, with the goal of making MIP modelling easier. A big step is the incorporation of predicates, such a “cycle” which encapsulate MIP sub-models. This talk explores the impact of such predicates in the MiniZinc modelling language when it is used as a MIP front-end. It reports on the performance of the resulting models, and the features of MiniZinc that make this possible.
Professor Mark Wallace is Professor of Data Science & AI at Monash University, Australia. We gratefully acknowledge support from a SICSA Distinguished Visiting Fellowship which helped finance his visit.
Professor Wallace graduated from Oxford University in Mathematics and Philosophy. He worked for the UK computer company ICL for 21 years while completing a Masters degree in Artificial Intelligence at the University of London and a PhD sponsored by ICL at Southampton University. For his PhD, Professor Wallace designed a natural language processing system which ICL turned into a product. He moved to Imperial College in 2002, taking a Chair at Monash University in 2004.
His research interests span different techniques and algorithms for optimisation and their integration and application to solving complex resource planning and scheduling problems. He was a co-founder of the hybrid algorithms research area and is a leader in the research areas of Constraint Programming (CP) and hybrid techniques (CPAIOR). The outcomes of his research in these areas include practical applications in transport optimisation.
He is passionate about modelling and optimisation and the benefits they bring. His focus both in industry and University has been on application-driven research and development, where industry funding is essential both to ensure research impact and to support sufficient research effort to build software systems that are robust enough for application developers to use.
He led the team that developed the ECLiPSe constraint programming platform, which was bought by Cisco Systems in 2004. Moving to Australia, he worked on a novel hybrid optimisation software platform called G12, and founded the company Opturion to commercialise it. He also established the Monash-CTI Centre for optimisation in travel, transport and logistics. He has developed solutions for major companies such as BA, RAC, CFA, and Qantas. He is currently involved in the Alertness CRC, plant design for Woodside planning, optimisation for Melbourne Water, and work allocation for the Alfred hospital.
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:
Agenda:
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.
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.
Title:
Predicting Drug Interactions with Kernel Methods
Abstract:
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.
References:
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
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.
Biography:
Jane Hillston was appointed Professor of Quantitative Modelling in the School of Informatics at the University of Edinburgh in 2006, having joined the University as a Lecturer in Computer Science in 1995. She is currently Head of the School of Informatics. She is a Fellow of the Royal Society of Edinburgh and Member of Academia Europaea. She currently chairs the Executive Committee of the UK Computing Research Committee.
Jane Hillston’s research is concerned with formal approaches to modelling dynamic behaviour, particularly the use of stochastic process algebras for performance modelling and stochastic verification. The application of her modelling techniques have ranged from computer systems, to biological processes and transport systems. Her PhD dissertation was awarded the BCS/CPHC Distinguished Dissertation award in 1995 and she was the first recipient of the Roger Needham Award in 2005. She has published over 100 journal and conference papers and held several Research Council and European Commission grants.
She has a strong interest in promoting equality and diversity within Computer Science; she is a member of the Women’s Committee of the BCS Computing Academy and chaired the Women in Informatics Research and Education working group of Informatics Europe 2016—2018, and during that time instigated the Minerva Informatics Equality Award.
Formal Approaches to Quantitative Evaluation
Qualitative evaluation of computer systems seeks to ensure that the system does not exhibit bad behaviour and is in some sense “correct”. Whilst this is important it is also often useful to be able to reason not just about what will happen in the system, but also the dynamics of that behaviour: how long it will take, what are the probabilities of alternative outcomes, how much resource is used….? Such questions can be answered by quantitative analysis when information about timing and probability are incorporated into models of system behaviour.
In this short series of lectures I will talk about how we can extend formal methods to support quantitative evaluation as well as qualitative evaluation of systems. The first lecture will focus on computer systems and a basic approach based on the stochastic process algebra PEPA. In the second lecture I will introduce the language CARMA which is designed to support the analysis of collective adaptive systems, in which the structure of the system may change over time. In the third lecture I will consider systems where the exact details of behaviour may not be known and present the process algebra ProPPA which combines aspect of machine learning and inference with formal quantitative models.
Timetable:
Lecture 1: 9:30 – 10:30 – Performance Evaluation Process Algebra (PEPA)
Coffee break at 10:30 – 11:15
Lecture 2: 11:15 – 12:15 – Collective Adaptive Resource-sharing Markovian Agents (CARMA)
Lecture 3: 14:15 – 15:15 – Probabilistic Programming for Stochastic Dynamical Systems (ProPPA)
Venue: Upper and Lower College Halls
The next Scottish Programming Languages Seminar will take place in St Andrews on Wednesday March 13th.
All are welcome to attend, and the programme is available here.
Abstract:
Writing optimising compilers is difficult. The range of programs that may be presented to the compiler is huge and the system on which they run are complex, heterogeneous, non-deterministic, and constantly changing. Machine learning has been shown to make writing compiler heuristics easier, but many issues remain.
In this talk I will discuss recent advances in using deep learning to solve compiler issues: learning heuristics and testing compiler correctness.
Speaker Bio:
Hugh is a reader (associate professor) at the University of Edinburgh. His research involves all elements of compilers and operating systems, usually targeting performance and energy optimisation, often with a focus on using machine learning for those tasks. After his PhD, also at Edinburgh, he was a Fellow of the Royal Society of Engineering. Before returning to academia, he was an engineer at Microsoft and architect and team leader at Trilogy, delivering multi-million dollar projects to Fortune 500 companies.
Abstract:
2018 was a crazy year for privacy. The General Data Protection Regulation came into force in May, and new revelations on the personal data ecosystem were making headlines on a weekly basis. I will give the behind the scenes for a lot of these events, question why they didn’t happen earlier, and offer some thoughts on the necessary future of online services. This will include a brief discussion of topics such as semantic alignment, interpretable machine learning, or new privacy-preserving data processing techniques.
Speaker Bio:
Paul-Olivier Dehaye is a mathematician by training. Affiliated to the University of Zurich as a SNSF Assistant Professor until 2016, his career then took a turn towards data protection activism and social entrepreneurship. He was the researcher on several news articles who have reached millions of readers (including many with Carole Cadwalladr), and testified in front of the UK and EU Parliaments on multiple occasions. He is on the board of MyData Global, has founded the NGO PersonalData.IO, and the project MyData Geneva.
Abstract:
Escape rooms are popular recreational activities whereby players are locked in a room and must solve a series of puzzles in order to ‘escape’. Recent years have seen a large expansion technology being used in these rooms in order to provide ever changing and increasingly immersive experiences. This technology could be used to minimise accessibility issues for users, e.g. with hearing or visual impairments, so that they can engage in the same way as their peers without disabilities. Escape room designers and players completed an online questionnaire exploring the use of technology and the accessibility of escape rooms. Results show that accessibility remains a key challenge in the design and implementation of escape rooms, despite the inclusion of technology that could be used to improve the experience of users with disabilities. This presentation will explore the lack of accessibility within Escape Rooms and the potential for technology to bridge this gap.
Speaker Bio:
Dr Rachel Menzies is the Head of Undergraduate Studies for Computing at the University of Dundee and is the current SICSA Director of Education (https://www.sicsa.ac.uk/education/). She co-directs the UX’d research group (https://www.ux-d.co.uk/) and her research interests include user centred design with marginalised user groups, such as users with disabilities, as well as exploring novel interfaces, data visualisation and CS education. Her most recent work focusses on accessibility is in escape rooms, in particular how users with varied disabilities can access and enjoy the experience alongside typical users.
