Adam Fowler is a Senior Pre-Sales Engineer with MarkLogic Corporation. In the last ten years since graduating with a degree in Computer Science from Aberystwyth he has worked as a developer, dev team co-ordinator, and pre-sales engineer for a variety of small and large companies. These include Universities like Aberystwyth and Derby, and companies like FileNet, IBM and edge IPK. This has included working in Financial Services, Insurance, and the Public Sector, working with large partner SIs, and selling software across Europe, North America and South Africa.

Adam will start with a history of his first 10 years in industry, the highs and lows, covering small companies and large multi nationals. Then he will cover the Pre-Sales role and how to sell software generally, relating this to the types of activity a pre-sales engineer needs to carry out - and importantly why a pre-sales engineer is different to sales person. These sections include a few funny stories, including what not to ask a hotel concierge for! After this he will cover the software hype cycle - how software emerges in a new market, becomes popular, and then commoditised. He will finish by talking about Big Data, what it actually means, and the software that is currently trying to solve these issues - including Hadoop, NoSQL software and how OpenSource relates to Enterprise software vendors like MarkLogic. He will also briefly detail an academic challenge that MarkLogic is launching, that if you enter a project for could help pay toward your studies, and have you present your project to our customers. Hopefully this will give Computer Science students an idea of what trends will be waiting for them in industry, and open them up to Pre-Sales as a career option.

The collection of huge volumes of complex and deep, formally annotated, phenotype data from both systematic mutagenesis and hypothesis-driven studies using model organisms such as mouse and zebrafish, is being increasingly complemented by the formalisation of clinical phenotype annotation using the recently developed Human Phenotype ontology. The problems of comparing the similarity of phenotypes within and between species, especially for the effects of specific mutations, has been largely solved with a new approach to phenotype decomposition using species-agnostic ontologies, such as the Gene Ontology, and the Phenotype and trait ontology, which permit the relationship between phenotypes and diseases to be quantified and used for computational analysis. The application of this new approach will be illustrated with reference to the analysis of the human Mendelian overgrowth disorders and to the determination of the contribution to pathogenicity of genes contained within regions of human copy number (CNV) variation.

Motivated by the unceasing interest in hidden Markov models (HMMs), we re-examines hidden path inference in these models, using a risk-based framework. While the most common maximum a posteriori (MAP)/Viterbi path estimator and the minimum error/Posterior Decoder (PD) have long been around, other path estimators, or decoders, have been either only hinted at or applied more recently and in dedicated applications.

Over a decade ago, however, a family of algorithmically defined decoders aiming to hybridize the two standard ones was proposed by Brushe et. al. This and other previously proposed approaches will be shown to have various serious problems, and we will mention some practical resolutions of those.

Furthermore, simple modifications of the classical criteria for hidden path recognition will be shown to lead to a new class of decoders. Dynamic programming algorithms to compute these decoders in the usual forward-backward manner will be presented.

A particularly interesting subclass of such estimators can be also viewed as hybrids of the MAP and PD estimators. Similar to previously proposed MAP-PD hybrids, the new class is parameterized by a small number of tunable parameters. Unlike their algorithmic predecessors, the new risk-based decoders are more clearly interpretable, and, most importantly, work ``out-of-the box'' in practice, which is demonstrated on some real bioinformatics tasks and data. Some further generalizations and applications will be discussed in conclusion.

Regenerative medicine aims at developing new therapies and treatment for the currently non-curable diseases and conditions, and is a fast growing field in research, development and commercialisation. It mainly follows two inter-related approaches, tissue engineering and stem cell therapy. Regenerative medicine needs multidisciplinary effort involving physical and life scientists, engineers and clinicians.

Engineering plays an important role in the translation of regenerative medicine from laboratory to hospital bedside. In this presentation, examples of critical contribution of engineers will be discussed, including scale up or scale out, bioprocessing, control of stem cell differentiation, quality control etc. A specific example is the prediction of stem cell differentiation, where novel information technologies and computing, such as data mining and classification, can make a significant impact. The outcome can potentially save a lot of experimental effort and hence cost in time and money.

Algorithms have already changed the world and are continuing to do so. From the exploits of British cryptographers during WWII, through to the algorithms driving modern search engines, the common theme is the smart application of clever algorithms to help people. I'm going to try and illustrate by example how Google is carrying on this story and how today's students can continue it. To begin, I'll talk a bit about the founding of Google and PageRank. To finish, I'll talk about my personal experiences developing and deploying algorithms for Gmail's spam detection and the Priority Inbox. I'll also talk about how it's one thing to develop an algorithm that works for an individual, but something different to make it work for millions of users.

This talk is aimed at a broad audience from first years through to staff interested in machine learning.

Given their "selfish" genes, it is remarkable that biological creatures pay any attention to the displays, advertisements, threats, warnings, etc., directed at them, and equally remarkable that so many of these signals are produced in the first place. Moreover, many of these biological signals appear to be needlessly extravagant in terms of the energy spent, time taken, and risks incurred in producing them.

At first sight, for instance, it difficult to understand why peacocks persist in constructing and maintaining tails that are a significant and, to the disinterested observer, irrational drain on resources.
Might the same information not be conveyed through a stable signalling system employing much cheaper signals?

In this talk I will present an evolutionary simulation approach to answering such questions. Here, multiple artificial signalling systems are allwoed to compete with one another over evolutionary time, and where more than one signalling system is viable, the models explan why the more extravagant signalling systems will tend to be favoured by evolution.

Chris is a researcher in applied computing working in the School of Computing, University of Dundee, Scotland. Chris is involved in research, teaching and outreach in the School of Computing As well as the conventional computing of programming on a variety of platforms he is interested in how we make technology fit the people it's crafted for. Utilising tools such as focus groups, live theatre and ethnographic techniques , he is often not surprised to discover that the richness and complexity of the people we work and design for far outweigh the complexity of the technology we seek to construct.

In particular and the focus of his ongoing PhD research is an interest in computer programming and how programmers support themselves in solving problems. Where a programmer may be a senior analyst in large software development company or a primary school child first discovering that sequence, decision and repetition can make a robot dance. Can grounding the abstract components of a computer program in a physical device improve success in programming...?

[1] http://www.cs.bham.ac.uk/research/projects/cogaff/crp/
[2] http://www.cs.bham.ac.uk/research/projects/poplog/freepoplog.html
[3] http://www.cs.bham.ac.uk/~axs/my-doings.html

Much of Turing's work was about how large numbers of relatively simple processes could cumulatively produce qualitatively new large scale results e.g. Turing-machine operations producing results comparable to results of human mathematical reasoning, and micro-interactions in physico-chemical structures producing global transformations as a fertilized egg becomes an animal or plant. In the same spirit, this talk presents some aspects of a draft theory of "meta-morphogenesis": processes and mechanisms involved in interactions between changing environments, changing animal morphologies, changing information processing capabilities and changing mechanisms for producing all these changes.

"Informed control", is a core feature of all life, starting with control of various kinds of physical behaviour, then later also informed control of information-processing in individuals, groups of individuals in one or more species, and in larger more abstract systems. By understanding the varied pressures leading to these changes and the many and varied results of such changes, we can gain new insights into issues addressed in a variety of disciplines, including computer science, AI/Robotics, cognitive science, neuroscience, psychology, psychiatry, linguistics, philosophy and education. I'll try to show (if time permits) how some of what we have learnt about types of information processing system in the last half century or so can illuminate philosophically puzzling features of animal minds, including the existence of "qualia", minds with mathematical intelligence, and the roles of precursors of human language required for perception, motivation, planning, plan execution, learning, and later development of languages for communication. This should also enhance our still incomplete understanding of requirements for future machines rivalling biological intelligence. One of many implications is the short-sightedness of some current theories of "embodied" cognition.

In 1990, a typical Sun system had a single 10mhz cpu and 4MB of memory and might have run in the region of 30-50 processes. Today Oracle ships systems with 512 cpu's and 4TB of memory and such systems have a few

100,000 processes at most. The roadmap suggests that by 2015 this will rise to cpu counts around 13,000 and 150TB of memory to serve workloads in excess of 1 million processes.

Like C.S.I., when a system fails a post-mortem is required. A crash dump is the body, an image of the system memory at the time of failure. This talk looks at technical, logistical and tools challenges of diagnosing system failure after the fact when the body is 4TB in size and the challenges of scaling post-mortem failure diagnosis to ever larger configurations.

Interactive exploration of time series data has to face challenges coming from the increasing of both data size and richness of carried information.

A third parallel issue, of particular relevance to visualization, comes from the inherent human limitations to process large amount of information, aspect which seriously constrains visual display of data.

These three factors currently dominate the design of new visualization solutions to support the exploration process of time-series data in search of interesting features and trends./

In this talk we will present an overview of work developed at Swansea University to tackle these challenges.

Three majour results in the fields of video visualization and remote sensing data analysis will be presented together with some future directions and open questions.

Biology is rapidly being re-shaped as a data-intensive science as biologists are faced with ever increasing challenges from both the scale and complexity of the data being generated by transformational technologies such as next generation genome sequencing techniques.

Furthermore, the adoption of systems approaches to biological research to address some of the grand challenges in medicine and agriculture require many diverse types of complex data to be brought together in ways that were not previously envisaged. These challenges bring data integration techniques to the fore as one of the unsolved problems for

Bioinformatics. In this seminar I will introduce the open source graph-based Ondex data integration and visualisation system that we have been developing at Rothamsted and show examples of how it has been used in a range of systems biology projects by solving practical problems in syntactic and semantic data integration.

The Linux kernel grows at over a line a minute, and a line of code changes every fifteen seconds. Releases are done about quarterly and there is no separate long term development codebase.

This seminar explores the history of the kernel development process and how over a thousand people with no formal management structure continually re-engineer a complex system.

In addition to navigation, autonomous localisation and autonomous scientific sample acquisition will be required. Scientists want to go to new locations on Mars where current wheeled locomotion is impractical, and aerobot technology is becoming a real possibility for future missions. Issues such as planetary robot survivability and longevity are key research areas that need to be addressed. As always these challenges are set against the `ideal' engineering requirement for zero mass, zero power and zero volume. This presentation will focus upon the future challenges for planetary robotic exploration from 2010 to 2030 and beyond.

After the seminar there will be

Refreshments at 5:30pm followed by BCS AGM and talk at 6pm