Postdoctoral researcher, Sheffield Hallam University.
New hardware applications, high speed video optimisation and neural networks, biomechanics and motion analysis, impact mechanics.
I am interested in enhancing the lives of others through the use of affordable consumer technologies. Competition in consumer electronics has always pushed forward technology and driven down prices. Within the last five years, a number of developments have given the average consumer access to devices which only a decade ago would have cost tens of thousands of pounds.
The Microsoft Kinect (released at the end of 2010) has created new research activity in a number of different areas. The £100 computer camera is able to see depth and uses this information to track body position, automatically detect objects and scan objects in 3D. Within a year of its release, universities were showcasing new applications in robotics and artificial intelligence.
As a researcher interested in engineering, sport and biomechanics, I’m working to adapt the Kinect and devices like it into human analysis tools for use in sport, education and health. These areas have so far been ignored, but our research group is exceptionally well placed to deliver them.
Our current work includes developing fast and accurate body scanners for use in rehabilitation after surgery, body tracking systems for sports coaching, and gesture controlled virtual environments for the infirm and elderly. All these applications use a combination of hardware and custom software in combination with the Microsoft Kinect (although we envisage future generations of the hardware to be even more powerful, and fully intend to use it). I intend to create all software as open access, so that anyone with the appropriate hardware (a computer and Kinect) can benefit from these tools.
Scientific Statistician, Rothamsted Research.
Analysis and interpretation of molecular sequence data from both crop plants and pathogens that affect crops, statistical analysis of molecular sequence data, development of software for the statistical analysis of molecular sequence data, the study of complex communities of microorganisms based on samples of DNA material.
The research area that I am most interested in is the study of the total genomic content of microbial communities. In metagenomics (as this field of study is called), DNA material is sampled collectively from the microorganisms that populate the environment of interest (e.g. agricultural soil, ocean water, or the human gut). The extracted DNA sequences are subsequently used to profile the environment and its biodiversity, its dominant microbial classes or biological functions, and whether and how this profile differs from those of other environments.
The impact of metagenomics in our understanding of the natural world has been revolutionary and profound. Insights derived from metagenomics studies have become increasingly relevant in areas as diverse as human health (evaluation of antibiotic effects and other drugs) and biodefense (monitoring of food, air and water quality).
Other fields that have benefited from metagenomics contributions include: medical and epidemiological sciences (developing disease diagnosis and treatment strategies based on the composition of bacterial and viral communities in gut, dental caries, tumours and skin) and bioenergy (advancing technologies that process crops or waste with microbial systems to generate renewable forms of energy). In my role as a statistician at Rothamsted Research, I collaborate closely with micro-biologists in the metagenomic study of soil microbial communities. This is crucial to better manage agricultural soils and minimise the negative impact of agricultural practices.
Lecturer in Coastal Engineering, University of Plymouth.
Fluid mechanics, transport processes, wave propagation into the nearshore, seabed and shoreline evolution, climate change effects on coastal erosion and reliability of coastal structures.
My main research interests focus on fluid mechanics, transport processes, wave propagation into the nearshore, seabed and shoreline evolution. All of these research interests are interrelated: ocean waves and currents interact with one another and as waves propagate into the shore they start interacting with the seafloor, affecting the wave direction and the wave height and shape. The wave and currents may lift sediment from the seabed and transport the sediment shoreward, seaward or alongshore through processes such as bedload transport, suspended load transport, and littoral drift. This in turn may cause the shoreline and the beaches to accrete or to erode.
My research aims to understand the different processes and how they affect the seabed and the shoreline evolution in the short and the long term. This is important since coastal erosion is likely to increase due to sea level rise and increased storminess linked to climate change. Coastal erosion leads to greater exposure of coastal and estuarine defences to storms, and hence to damage to these structures or to overtopping and flooding. Another aspect of my research is to extend current knowledge on reliability of coastal structures and how structural failure may occur under different wave loads, overtopping and erosion scenarios.
Clinical physicist, NHS Greater Glasgow & Clyde and the University of Aberdeen.
Artificial intelligence in medicine, knowledge-based systems, knowledge modelling & refinement hypothesis.
Intelligent computer programs generally consist of a knowledge base, a reasoning mechanism, and a suitable user interface. A knowledge base is often initially constructed with the help of a domain expert and then progressively refined into a high-performance knowledge base. As knowledge can change over time, a particular interest of the artificial intelligence (AI) community are methods for automatically suggesting refinements of a knowledge base.
I am currently exploring the use of argumentation logic in the automatic refinement of computerised knowledge bases. Argumentation logic is a formal framework for modelling human collaborative deliberations, allowing the exchange of arguments in favour, or against, some conclusion based on potentially incomplete or inconsistent information.
This work is being explored in the critical care medicine domain and is particularly important as medical knowledge is rapidly changing which is challenging for many clinical decision support systems (CDSS). It is hoped that if a CDSS presents a conclusion that a clinician disagrees with, the implementation of an argumentation engine in the CDSS will allow the reasoning applied by the CDSS to be made explicit, leading to a greater understanding by the clinician, and allow the clinician to enter into a dialogue with the CDSS, suggesting ways in which both the system’s reasoning and knowledge base can be improved. This research will broadly impact on the areas of computer science, cognitive science, and critical care research.
Research fellow, the University of Southampton.
Semantic web, agents, artificial Intelligence.
Large information sources, such as ontologies, can be invaluable in making decisions. However, the use of large ontologies can be expensive in terms of computation. This is particularly the case when there are computational limitations, such as mobile device constraints, memory limitations, and low or unreliable bandwidth, coupled with high costs associated with hosting, managing and using the data.
I am researching how to build a task-focused ontology automatically, using an online ontology evolution algorithm, so that information from large ontologies can be used, regardless of constraints. For my PhD, I developed two learning and one forgetting algorithm for evolving ontologies. This work was awarded a best paper and best student paper at two top-tier conferences on Agents (IAT 2010) and the Semantic Web (ISWC 2010), respectively. I was also awarded the Doctoral Prize for my PhD, which allows me to extend my research.
I am extending my forgetting algorithm so that it can predict which concepts will be the least useful, using a predictive model, which allows the algorithm to remove concepts earlier in a scenario than without prediction. This means that the ontology can remain smaller than other state-of-the-art approaches, thus outperforming them because of the reduced costs of using the ontology. I used prediction to improve my first learning algorithm, which saw an approximately 40% improvement, and I hypothesise using prediction with forgetting will see an improvement with a similar magnitude. I am also working on making my RoboCup OWLRescue software open source, because there is currently no platform for testing the performance of evolving ontologies.
PhD student, the Open University.
Scientific software development and the practices of the scientists who developing software, scientific software commercialisation and technology transfer.
My research interests evolve around scientific software development: I investigate the practices of scientists who develop software, the issues they encounter and their solutions. In particular, I am interested in scientist-developers who moved on from developing software solely for themselves to developing for others.
I think scientific community and software engineers who support scientific software development may learn a lot from the experiences of scientists who started as end-user developers and became developers for a wider community. I am also interested in scientific software commercialisation and technology transfer.
Scientists who decide to commercialise their software enter two, new areas: that of professional/commercial software development and business. In my research, I want to investigate and highlight how scientists could be supported in a successful transfer to the world of commercial software.
PhD student, University College London.
Sustainability, pro-environmental behaviour change, infrastructure, local government, policy, cities, international development
My research focuses on sustainable living and pro-environmental behaviour change. Fossil fuels are fast running out, and the clock is ticking for government to develop plans that will reduce the country’s carbon emissions - emissions that were legislated to be reduced by 80% before 2050 in the Climate Change Act 2008.
A considerable amount of this 80% reduction is allocated to behaviour change but little has been done to actually identify where these behaviour change savings will come from. To plug this gap in knowledge and better understand the nature of pro-environmental behaviour change and how it can be generated and measured, my research aims to understand how local government can stimulate pro-environmental behaviour change in the borough population and what influence this has on the overall environmental impact of the borough population.
Through sustainability projects and interventions, local authorities can stimulate pro-environmental behaviour change. My research aims to understand what affects the success of these interventions and measure the change in behaviour can actually be generated by these projects. I am working to understand the effect that the delivery approach can have on the success of an intervention. For example, do top-down, government-led projects work better than community-led, council-facilitated projects in stimulating pro-environmental behaviour change?
To assess the effectiveness of these interventions in reducing the overall environmental impact of the population, it will be necessary to understand the impact that these changes in pro-environmental behaviour have on the environment. Presently behavioural changes rely greatly on assumptions; this research aims to accurately measure the impact of individual projects to find out whether these assumptions are accurate or misplaced. Measuring this impact is an area of my research that I am working on in collaboration with various councils in London.
Postdoctoral researcher, British Antarctic Survey.
Glaciology and glacial geomorphology, subglacial processes and in situ instrumentation, interactions between long-term landscape, ice sheet and climate evolution, polar environments and the history of polar exploration.
The Antarctic continent has played a key role Earth’s history, influencing the movement of the continents through plate tectonics and affecting global climate. By examining the land beneath the ice we can start to build a picture of how climate and ice have evolved and affected the continent over geologic time scales.
My research is centred on the Gamburstev Subglacial Mountains, a mountain range the size of the Alps, hidden beneath the ice in the centre of the East Antarctica. These mountains are important because coupled ice-sheet and climate models predict that they were a key nucleation site for the initiation of the East Antarctic Ice Sheet, 34 million years ago. However, the nature of this early glaciation and subsequent ice sheet development is poorly known and evidence for these processes is largely restricted to the continental margins.
To investigate these mountains, I use airborne ice penetrating radar data from the AGAP project, which is a recent international airborne geophysical survey that was carried out during the International Polar Year. The survey provides some of the first data from the interior of East Antarctica, where previously none had existed.
Currently, I am examining the geometry of the landscape in order to quantify the processes, patterns, and scales of landscape evolution across the mountains. This will provide critical new information on the different stages of landscape development in Antarctica and the resultant impact of long-term climate evolution and ice dynamics.
Head of Bioinformatics, MRC Human Genetics Unit.
My work involves computational analyses of large genomics, transcriptomics and epigenomics datasets to address questions about human evolution and our predisposition to disease.
Recent developments in high-throughput sequencing have led to huge volumes of new data - consequently it's a great time to be a computational biologist. These new data relate to the variations between individuals and species, variations at the level of our genome (DNA) sequences but also at the level of transcription, as our genes are expressed and perform their functions within our cells. More recently we have accumulated additional data at another complementary level of biological complexity: chromatin structure. Chromatin structure is the physical landscape of the genome, describing the packaging of our DNA as it is wound around repeating protein complexes, called nucleosomes, then further compacted and folded into higher order structures. The various layers of packaging result in a dynamically varying landscape, or epigenome, that plays important roles in many biological processes.
We study the current avalanche of sequencing data computationally to learn more about human biology. We are interested in how the genomic variations we all carry interact with chromatin structure, gene expression and the complex cellular networks regulating genes. We want to discover how these structures and networks relate to the human traits we are all familiar with, such as eye colour, height, etc. We would like to know how they act during embryonic development and how they change during human disease processes. We also want to understand their roles in the evolutionary history of our species.