The University of Southampton
×
Electronics and Computer Science

Published: 27 February 2019
Illustration
The Software Sustainability Institute brings together a team of experts from the universities of Southampton, Edinburgh, Manchester and Oxford.

A national research software facility that is co-directed within the University of Southampton has been awarded £6.5 million to continue strengthening software practices that underpin the UK’s world-class research.

The Software Sustainability Institute (SSI) will build upon its work helping thousands of researchers across all disciplines through the funding boost from all seven of the UK’s Research Councils.

The Institute, which brings together a team of experts from the universities of Edinburgh, Manchester, Oxford and Southampton, will focus its new phase on the creation of self-supporting communities that will facilitate the adoption of better practices and the sharing of expertise across the entire research community.

Dr Simon Hettrick, SSI Deputy Director and Co-Director of the Southampton Research Software Group, says: “Around 70% of research relies on software and its use has become near ubiquitous across all disciplines. We broke new ground when we founded the Institute and our work over the last eight years has elevated the topic of software in research so it is now a priority issue for most, if not all, research stakeholders – and we’ve achieve this feat not just in the UK but around the world.

“This next phase of funding demonstrates the rapidly growing interest in the field. It allows us, over the next five years, to scale out our successful programmes to reach an even larger audience. This means more researchers following good software practices, which gives us an important edge in an ever more competitive research environment.â€?

Since 2008, the SSI has built a network of over 110 Fellows, published more than 80 guides read by over 250,000 people, organised more than 180 training events for over 4,500 learners, provided support to over 60 different research projects from across all research councils, and succeeded in the creation and advocacy of the Research Software Engineer role.

The University of Southampton supports the Institute through Simon’s input as Deputy Director and the participation of four Research Software Engineers and one Community Coordinator.

The new SSI phase has been funded by the Arts and Humanities Research Council (AHRC), the Biotechnology and Biological Sciences Research Council (BBSRC), the Economic and Social Research Council (ESRC), the Engineering and Physical Sciences Research Council (EPSRC), the Medical Research Council (MRC), the Natural Environment Research Council (NERC), and the Science and Technologies Facilities Council (STFC).

Articles that may also interest you

The Cyber Security Academy training courses

Hampshire and Sussex Cyber Cluster launched to strengthen regional cybersecurity

Breathing sensor 'sticker' could help save lives
Share this article FacebookTwitterWeibo

Published: 27 February 2019
Illustration
The AccelerComm team are promoting the spinout this week at the Mobile World Congress in Barcelona.

University of Southampton spinout AccelerComm has completed a £2.5 million funding round to deliver faster speeds in a wireless connected world.

The investment will scale further development and commercialisation of IP solutions that improve the latency, throughput and error correction of next generation communications.

The semiconductor IP spinout, launched from expertise in the School of Electronics and Computer Science, has closed its latest round led by Bloc Ventures and IP Group.

AccelerComm’s LDPC, polar and turbo FEC technologies deliver error correction decoding that is required to overcome the effects of noise, interference and poor signal strength. This enables optimum performance for everyday devices such as phones and tablets that rely on 4G LTE, 5G New Radio (NR) and MIMO cellular communications where low power, small footprint and ultra-low latency are all key requirements.

Professor Rob Maunder, AccelerComm founder and Chief Technology Officer, said: “Flexibility is key to success when developing advanced communications and these high performance standardised architectures in the wireless infrastructure market are enabling that flexibility while helping to reduce development time.â€?

Engineers from the spinout are demonstrating their latest software-only IP for 5G NR in support of virtualised networks on the Great Britain and Northern Ireland stand of the Mobile World Congress (MWC) in Barcelona this week.

AccelerComm’s optimised polar coding chain is already in commercial use today in 5G NR products, less than eight months after a release of 5G NR specification was concluded by 3rd Generation Partnership Project (3GPP).

Through this latest investment Bloc Ventures - which was founded by industry veterans from Arm Holdings and Vodafone - will take a seat on the AccelerComm board.

Tom Cronk, AccelerComm’s newly appointed CEO and former Arm executive, said: “This investment supports and underwrites our strategy to meet the demand in a growing wireless connected world for products that can increase communications performance without compromising on size and power.â€?

To find out more about the spinout please visit: www.accelercomm.com.

Articles that may also interest you

The Cyber Security Academy training courses

Hampshire and Sussex Cyber Cluster launched to strengthen regional cybersecurity

Breathing sensor 'sticker' could help save lives
Share this article FacebookTwitterWeibo
https://www.ecs.soton.ac.uk/news/6414

Labs officially opened by Sir Robin Saxby

Sir Robin Saxby, founding CEO of Arm, officially opened the new labs. He gave a seminar on technology and entrepreneurship to a select group students from across the University and joined industry partners on a tour of the new spaces.

Read more here

Contact

Telephone: +44 (0) 23 8059 2422

Email: bdr1g15@soton.ac.uk

Share this profile FacebookTwitterWeibo
Telephone:
+442380598866
Email:
dkb@it-innovation.soton.ac.uk

 

Dan is a Research Engineer at the IT Innovation Centre within the School of Electronics and Computer Science, University of Southampton. After a PhD in Theoretical Particle Physics from the University of Manchester completed in 2015, with a short post-doc in Particle Cosmology in 2016, he joined the IT Innovation Centre in 2017.

He has an intimate familiarity with the data science pipeline: from the extraction of meaningful data and pre-processing, to the critical evaluation of results from machine learning algorithm studies and the deployment of machine-learned models. He is primarily driven by the vast application domain of statistics, particularly where cross-disciplinary work is involved, and has extensive experience in statistical analysis and the development of Monte-Carlo simulations.

In the COVID-19 era, Dan has undertaken several projects to assist with the UK's national epidemic response:

  • Prediction of COVID-19 hospital impact using infectious disease epidemic modelling: fitting stochastic epidemic models to time series of hospitalisations to infer hospital impact
  • The statistical evaluation of new and emerging testing technologies to identify SARS-CoV-2 such as lateral flow devices and loop mediated isothermal amplification (LAMP) based technologies
  • Development of models of regular testing, taking into account the epidemiology and virology of SARS-CoV-2, sociological factors of adherence to testing and isolation, operational and logistical characteristics of the programme, and then determining how a given isolation policy subsequently affects transmission
  • Development of machine learning models to predict COVID-19 patient deterioration

In addition to COVID-19 work, Dan has worked on various projects. Historically, his work has been on the following research projects:

CrowdHEALTH (EU H2020 project): CrowdHEALTH intends to integrate high volumes of health-related heterogeneous data from multiple sources and develop data analytics tools to operate on the integrated data, with the aim of supporting policy making decisions. Dan’s work in this project is in the development of machine learning prediction algorithms to predict when patients will have the first diagnosis of a set of high-impact diseases, such as cardiovascular disease and chronic kidney disease, in a timely and accurate fashion such that one can construct possible preparation and prevention approaches. The project utilises the prediction algorithm for population risk stratification.

BigMedilytics (EU H2020 project): BigMedilytics aims to transform the EU healthcare sector by using state-of-the-art big data technologies to achieve breakthrough productivity in the sector by reducing cost, improving patient outcomes and delivering better access to healthcare facilities simultaneously. Dan’s work in this project revolves around studying self-reported patient data associated with chronic obstructive pulmonary disease, linked with weather and pollution data in the patient’s home environment, to predict COPD exacerbation events. He is currently working on a machine learning algorithm to predict when these events will occur based on the linked data, so that patients can either prepare for the event by obtaining medication, or by preventing the event altogether.

Dan has also completed several pieces of commercially funded work as part of the VIVACE accelerated capability environment, as well as direct projects from the UK Home Office.

Research

Research interests

Dan’s research interests spans multiple fields in computer science and statistics.

In statistics, his core interests are the following:

  • Development of more effective sampling techniques for producing samples from multivariate distributions
  • Development of metrics for the assessment of statistical conditional models/machine learning models as applied to data
  • The synthesis of Monte-Carlo simulation with machine learning and statistical learning

In data science, his core interests are in the following:

Missing data approaches: the vast majority of real-world applications of machine learning methods involve the problem of missing data. Imputation is one the most common approaches to handling missing data, and is practically useful. However, for accurate imputation, one needs a lot of data, as the imputation problem is often a more difficult problem to solve than the machine learning task at hand. Dan has great interest in developing approaches to missing data that do not involve imputation and exploits the full data in other manners, either through the modification of existing machine learning algorithms, or by the creation of new ones.

Electronic Patient Record event prediction: having an advanced knowledge of certain unfavourable medical events occurring, such as the first diagnosis of a high-impact disease or medical events that can repeat such as myocardial infarction, for individual patients can be used to either prepare for the event’s occurrence, or prevent it altogether. Dan is interested in developing approaches to predict these events, using either statistical analysis or machine learning methodologies, and developing models of disease progression. He is primarily interested in approaches that are generic, i.e., using electronic patient record datasets to determine the most significant factors affecting the events of interest rather than explicit domain expertise.

Other interests within data science includes:

  • Development of new classification techniques for the exploration of scientific data
  • Time series, image, audio/speech specific feature extraction and classification approaches

Dan holds other research interests outside of data science and statistics, including cryptography and efficient fully homomorphic encryption schemes for secure cloud computation.

Publications

Grace, Paul, Burns, Daniel, Neumann, Geoffrey, Pickering, Brian, Melas, Panagiotis and Surridge, Michael (2018) Identifying privacy risks in distributed data services: A model-driven approach. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE. 1513 - 1518 . (doi:10.1109/ICDCS.2018.00157).

Neumann, Geoffrey, Grace, Paul, Burns, Daniel and Surridge, Michael (2019) Pseudonymization risk analysis in distributed systems. Journal of Internet Services and Applications, 10 (1). (doi:10.1186/s13174-018-0098-z).

Burns, Daniel, Karamitsos, Sotirios and Pilaftsis, Apostolos (2016) Frame-covariant formulation of inflation in Scalar-Curvature theories. Nuclear Physics B, 907, 785-819. (doi:10.1016/j.nuclphysb.2016.04.036).

Burns, Daniel and Pilaftsis, Apostolos (2015) Matter quantum corrections to the graviton self-energy and the Newtonian potential. Physical Review D, 91 (6), [064047]. (doi:10.1103/PhysRevD.91.064047).

Chmiel, Francis, Burns, Daniel, Pickering, Brian, Blythin, Alison, Wilkinson, Thomas and Boniface, Michael (2020) Retrospective development and evaluation of prognostic models for exacerbation event prediction in patients with Chronic Obstructive Pulmonary Disease using data self-reported to a digital health application. The Lancet Digital Health. (doi:10.1101/2020.11.30.20237727). (Submitted)

Chmiel, F. P., Azor, M., Borca, F., Boniface, M. J., Burns, D. K., Zlatev, Z. D., White, N. M., Daniels, T. W.V. and Kiuber, M. (2020) Identifying those at risk of reattendance at discharge from emergency departments using explainable machine learning. medRxiv. (doi:10.1101/2020.12.02.20239194).

Ptasinska, Anetta, Whalley, Celina M., Bosworth, Andrew, Poxon, Charlotte, Bryer, Claire, Machin, Nicholas, Grippon, Seden, Wise, Emma L, Armson, Bryony, Howson, Emma L. A., Goring, Alice, Snell, Gemma, Forster, Jade, Mattocks, Christopher, Frampton, Sarah May, Anderson, Rebecca, Cleary, David, Parker, Joe, Boukas, Konstantinos, Graham, Nichola, Cellura, Doriana, Garratt, Emma, Skilton, Rachel, Sheldon, Hana, Collins, Alla, Ahmad-Saeed, Nusreen, Friar, Simon, Burns, Daniel, Williams, Tim, Godfrey, Keith, Deans, Zandra, Douglas, Angela, Hill, Sue, Kidd, Michael, Porter, Deborah, Kidd, Stephen P., Cortes, Nicholas J, Fowler, Veronica L., Williams, Tony, Richter, Alex G. and Beggs, Andrew D. (2021) Diagnostic accuracy of Loop mediated isothermal amplification coupled to Nanopore sequencing (LamPORE) for the detection of SARS-CoV-2 infection at scale in symptomatic and asymptomatic populations. Clinical Microbiology and Infection, 27 (9), 1348.e1-1348.e7. (doi:10.1016/j.cmi.2021.04.008).

Duckworth, Christopher, Chmiel, Francis P., Burns, Daniel, Zlatev, Zlatko D., White, Neil M., Daniels, Thomas W. V., Kiuber, Michael and Boniface, Michael J. (2021) Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19. Scientific Reports, 11 (1), [23017]. (doi:10.1038/s41598-021-02481-y).

Boniface, Michael, Burns, Daniel, Duckworth, Chris, Duruiheoma, Franklin, Armitage, Htwe, Ratcliffe, Naomi, Duffy, John, O’Keeffe, Caroline and Inada-Kim, Matt (2021) COVID Oximetry @home: evaluation of patient outcomes. medRxiv. (doi:10.1101/2021.05.29.21257899).

Kidd, Stephen, Burns, Daniel, Armson, Bryony, Beggs, Andrew D., Howson, Emma L. A., Williams, Anthony, Snell, Gemma, Wise, Emma L., Goring, Alice, Vincent-Mistiaen, Zoé, Grippon, Seden, Sawyer, Jason, Cassar, Claire, Cross, David, Lewis, Tom, Reid, Scott, Rivers, Samantha, James, Joe, Skinner, Paul, Banyard, Ashley, Davies, Kerrie, Ptasinska, Anetta, Whalley, Celina M., Poxon, Charlie, Bosworth, Andrew, Kidd, I. Michael, Richter, Alex G., Burton, Jane, Love, Hannah, Fouch, Sarah, Tillyer, Claire, Sowood, Amy, Patrick, Helen, Moore, Nathan, Andreou, Michael, Laxman, Shailen, Morant, Nick, Clark, Duncan, Walsh, Charlotte, Houghton, Rebecca, Parker, Joel D, Slater-Jefferies, Joanne, Brown, Ian, Deans, Zandra, Porter, Deborah, Cortes, Nicholas J., Godfrey, Keith, Douglas, Angela, Hill, Sue and Fowler, Veronica L. (2021) RT-LAMP has high accuracy for detecting SARS-CoV-2 in saliva and naso/oropharyngeal swabs from asymptomatic and symptomatic individuals. (doi:10.1101/2021.06.28.21259398).

Chmiel, F. P., Burns, D. K., Azor, M., Borca, F., Boniface, M. J., Zlatev, Z. D., White, N. M., Daniels, T. W.V. and Kiuber, M. (2021) Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments. Scientific Reports, 11 (1), [21513]. (doi:10.1038/s41598-021-00937-9).

Kidd, Stephen P, Burns, Daniel, Armson, Bryony, Beggs, Andrew D, Howson, Emma L A, Williams, Anthony, Snell, Gemma, Wise, Emma L, Goring, Alice, Vincent-Mistiaen, Zoe, Grippon, Seden, Sawyer, Jason, Cassar, Claire, Cross, David, Lewis, Thomas, Reid, Scott M, Rivers, Samantha, James, Joe, Skinner, Paul, Banyard, Ashley, Davies, Kerrie, Ptasinska, Anetta, Whalley, Celina, Ferguson, Jack, Bryer, Claire, Poxon, Charlie, Bosworth, Andrew, Kidd, Michael, Richter, Alex, Burton, Jane, Love, Hannah, Fouch, Sarah, Tillyer, Claire, Sowood, Amy, Patrick, Helen, Moore, Nathan, Andreou, Michael, Morant, Nick, Houghton, Rebecca, Parker, Joe, Slater-Jefferies, Joanne, Brown, Ian, Gretton, Cosima, Deans, Zandra, Porter, Deborah, Cortes, Nicholas J, Douglas, Angela, Hill, Sue L, Godfrey, Keith M and Fowler, Veronica L (2022) Reverse-transcription loop-mediated isothermal amplification has high accuracy for detecting severe acute respiratory syndrome coronavirus 2 in saliva and nasopharyngeal/oropharyngeal swabs from asymptomatic and symptomatic individuals. Journal of Molecular Diagnostics, 24 (4), 320-336. (doi:10.1016/j.jmoldx.2021.12.007).

Chmiel, Francis P, Burns, Dan, Pickering, John Brian, Blythin, Alison, Wilkinson, Thomas MA and Boniface, Michael J (2022) Prediction of chronic obstructive pulmonary disease exacerbation events by using patient self-reported data in a digital health app: statistical evaluation and machine learning approach. JMIR Medical Informatics, 10 (3), 11, [e26499]. (doi:10.2196/26499).

Contact

Telephone: +44 (0) 23 8059 8866

Email: dkb@it-innovation.soton.ac.uk

Share this profile FacebookTwitterWeibo

Internet of Things

Gain the knowledge and skills to develop and engineer the next generation of IoT devices and systems

Internet of Things

Gain the knowledge and skills to develop and engineer the next generation of IoT devices and systems

Pages