Centre for Data Science Postgraduate Research Brochure

Centre for Data Science (CDS) Postgraduate Research

Research Coventry

discover more online www.coventry.ac.uk/research

Welcome to the Centre for Data Science (CDS) Data is at the heart of all of science and engineering, influencing the models we choose, the policies we implement and the decisions we make. The data revolution means that data has moved from a validation tool to being an object of study in its own right.

The Centre for Data Science (CDS) brings to bear expertise in statistics, computer science and artificial intelligence (AI), engineering, mathematics and physics to find fundamental solutions to data inspired problems across all areas of science and engineering. In particular, in medicine and healthcare; computer vision and natural image processing; and cyber-physical systems with applications in communications, transport and humanitarian engineering. CDS is also involved in developing the next generation of algorithms and models.

CDS takes its fundamental research and applies it to real world problems.

Professor Damien Foster BSc (Hons) (Edin) DPhil (Oxon) FIMA MINSTP

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About CDS CDS develops fundamental research in the areas of data science, AI, computer science and statistics, and applies this in application areas across science and engineering, including (but not limited to): ▪ Biological sciences ▪ Health ▪ Finance ▪ Digital arts ▪ Transport As such, the centre operates according to a multidisciplinary research approach that is unique to the field.

A postgraduate researcher (Gene Palencia) soldering an external voltage monitor to a solar lantern PCB, as part of the design of Mobile Solar Lantern Monitoring System under the HEED project.

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CDS research themes CDS is currently organised according to four themes.

AI for cyberphysical systems (CPS) A cyberphysical system (CPS) is one involving computer-based sensing and control of a physical mechanism, typically also involving some form of human interaction. Key problems in CPS involve: ▪ making the CPS device sufficiently small and power-efficient to suit the application

Additionally, large networks of communicating devices can lead to a range of interesting problems covering topics including: ▪ graph theory and information theory ▪ data security ▪ optimisation in local versus remote processing of data.

▪ correctly estimating the state of the underlying physical (or social) system ▪ acting optimally in the context of the application ▪ naturally, many current ideas from AI, including deep learning and reinforcement learning, are applicable to these problems and provide opportunities for significant improvement over current approaches.

There is a big focus on real-world problems, such as providing a thermally comfortable but sustainably energyefficient environment in a house or vehicle cabin, or understanding the social and economic aspects of energy use in populations of displaced people.

We engage with industry leaders and non-government organisations to deeply understand their domain. Our chief aim is to provide a benefit back to those we interact with.

An example of CDS's approach can be seen in the Post-Earthquake Structural Health Monitoring System (PE-SMS), an end-to-end proof of concept wireless sensor network for the collection, communication and aggregation of structural health data to help engineers target buildings that show signs of distress.

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Machine learning for computer vision and natural language processing Machine learning has changed major industries around the world, and its impact is felt in many areas of our day-to-day life. New machine learning methods are needed to tackle the big data world we live in, especially in challenging areas such as computer vision and natural language processing. Such new methods need to effectively train very large models, usually in the form of deep neural networks or large probabilistic models. This is a big challenge both algorithmically and in its implementation on high-performance computers.

Research on deep and other machine learning methods is done in the context of: ▪ real-world computer vision problems from biomedical and health applications ▪ smart cities and autonomous driving ▪ challenging natural language processing tasks.

All themes contribute to solutions for six application areas.

▪ ▪ ▪ ▪

Healthcare and improving quality of life Health and life sciences Industry 4.0 Smart cities and transport

▪ The future of computing ▪ Novel algorithms and the use of data science in science and technology

Fundamental algorithms for AI

Statistical and computational modelling

CDS’s approach is to take fundamental scientific ideas in AI and data science and apply these to real-world datadriven problems. However, successful applications rely on the development of new theory, algorithms and implementations to underpin these techniques, and so CDS has a dedicated focus here.

Complex systems in nature and technology often consist of many simple components, which combine to show complex collective behaviour.

These systems require a combination of statistical, mathematical and computational modelling techniques to understand their behaviour. We use a combination of process-driven and data-driven modelling to develop powerful strategies to understand and predict these complex behaviours.

We have expertise on many algorithms for both classical machine learning and deep-learning technology. We are interested in both high-level algorithm design to give more powerful AI, and also low-level circuit implementations to better support and adapt to crucial techniques like artificial neural networks.

Our interests extend also to the application of AI techniques to further other fundamental areas of science, including: ▪ symbolic computation and computer algebra systems to automate exact mathematics ▪ SAT/SMT-solvers for constraint checking and verification ▪ topological data analysis to provide insight on the shape of potentially large and noisy data ▪ new approaches to NP-Hard problems through the use of novel meta-heuristics.

In addition to data-driven techniques, as embodied by machine learning, we use reverse engineering, as well as mathematical and statistical modelling techniques to analyse real-world problems in the bio-sciences and engineering.

Whilst data-driven techniques are described as ‘model-free’, they are in fact ways of optimising model parameters, and the quality of the output is only as good as the underlying model.

Current projects for this theme ▪ Understanding and predicting the onset of neurological illnesses such as Alzheimer’s and Parkinson’s disease.

▪ Investigation of the psychological impact of flooding in the UK. ▪ Spatio-temporal modelling of HIV, Cholera and COVID-19 for propagation, prediction and policy decisions.

▪ Prediction of pedestrian behaviour to inform connected-vehicle control.

▪ Modelling of protein dynamics during translocation across cell membranes.

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Our research environment CDS is divided into four overlapping themes that work together to provide a rich scientific environment to allow researchers from different scientific backgrounds to discover new opportunities for collaboration, both inside and outside of CDS. The mission of CDS is to develop new science to support application areas across Coventry University's (the university) portfolio, which gives rich opportunities to collaborate with researchers across all areas of science, engineering and social sciences on problems that matter in today’s data-driven world.

CDS benefits from high-class, highperformance computing facilities which are regularly updated, and the university is investing in high-quality 'Internet of Things' development and demonstration facilities as well as a collaborative environment for researchers.

A fertile, multi-disciplinary and internationally focused research culture will give prospective postgraduate and postdoctoral researchers a good environment in which to develop their research skills and their careers in a supportive environment.

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Postgraduate research: what we offer ▪ There are a number of ongoing funded/selffunded opportunities available.

▪ CDS allocates each PhD student a postgraduate tutor who is not one of the student’s supervisory team.

▪ Postgraduate researchers are encouraged to organise seminars and reading groups relevant to their subject areas.

▪ CDS's doctoral training activities include distance learning programmes, and so a number of these activities will be available by live streaming or recorded for our students abroad.

▪ The research community within CDS benefits from the excellent facilities offered internally and also within the wider Faculty of Engineering, Environment and Computing.

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www.coventry.ac.uk/CDS

J1679-20 Š Coventry University.

Postgraduate research enquiries E: cds@coventry.ac.uk