Meet the team (scientific computing)

Matt set up the scientific computing group in imaging to future proof our digital innovations by bringing together the skills and systems required to deliver software as a medical device under a suitable quality system. He is a SOAR research leaders programme fellow, focussed on digital diabetes technologies and building a network of researchers and clinicians to answer some of the most pressing research questions around the rapidly expanding access and capabilities of technologies aiding glucose management. His work has focused on complex patient groups, including those transitioning from paediatric to adult healthcare systems, and those transitioning from self to supported glucose management, including those living with dementia and in a care home setting. Matt is also the regional technology co-lead for the Children and Young People Diabetes Network, is part of Diabetes UK’s leadership community, and has contributed to the PRSB (Professional Records Standards Body) diabetes standards. He is also technical manager for NHS England's Core 20 plus 5 programme tackling inequalities in diabetes technologies in children and young people across the South East.

Matt started his career at the Institute of Cancer Research (ICR) and Royal Marsden, developing patient-specific 3D dose planning systems for molecular radiotherapy (MRT), transferring this work to patient-specific drug development and targeting, gaining a PhD from ICR, before moving into digital health and a Masters in healthcare design from Imperial College and the Royal College of Art.

Email matthew.guy@uhs.nhs.uk

James is a clinical scientist and is QMS lead for the medical device software group as well as leading our data management and curation services. As part of this role, he leads and supports a number of projects both within the team, across the wider organisation and regionally. This includes supporting clinical safety for regional pathology network's digital deployments, as well as being a part of national networks for clinical scientific computing and AI in healthcare. He leads and supports a wide variety of medical device software development projects such as spectroscopy processing, kidney diagnosis decision support, perfusion2diagnosis and the SORT app. He has a passion for deriving insights from medical data, making data processing more efficient through increasing automation and enabling our local patients to access the latest developments as quickly and safely as possible.

Before joining UHS, James completed the Scientist Training Program and worked in the scientific computing team at the Royal Surrey County Hospital. As part of this he undertook a MSc research project looking at the possibility of expediting breast cancer treatment pathways by using AI to predict the results of pathology tests using screening mammograms. He comes from a physics background, having completed his PhD in shockwave biophysics at Cranfield University.

Neil is a computer scientist, leading and supporting various projects in SciCom, including the technical setup and configuration of various systems that support our software development and increasing amounts of research work, as well as a variety of software as a medical device development, testing and deployment, including perfusion2diagnosis and the SORT app. As well as software development, he has a keen interest in a broad range of technical aspects of computing including introducing more automation both during software testing and in deployed systems where it can help provide consistent and timely results to clinicians and other colleagues.

Prior to joining the NHS, Neil completed a PhD in computational engineering at the University of Southampton. As a post-doctoral researcher he gained a lot of experience working at the university, mostly within the µ-VIS X-Ray Imaging Centre where he focused on an imaging research project for limited-angle CT scanning, also doing some internal system administration and development within µ-VIS, and supporting lab-based parts of undergraduate computing courses in engineering. He also worked in the Beamline Controls group at Diamond Light Source.

Deck joined the scientific computing team in September 2021 as a Scientist Training Programme (STP) trainee in clinical bioinformatics (physical sciences). He is currently in his final year of STP training and is working on his MSc research project, which focuses on using AI to predict the treatment response of peptide receptor radionuclide therapy (PRRT) in advanced neuroendocrine tumour patients.

Throughout his training, Deck has gained experience in the medical device software development lifecycle and working within a quality management system (QMS) accredited under ISO 13485. He has also developed an interest in using AI to address healthcare issues and employing computing solutions to automate routine clinical image processing. By automating clinical image processing, we can help save clinicians time and improve patient care, thereby reducing patient waiting times.

Riannach is a healthcare software developer, focusing on the practical applications of theory and research. In the group she works on software as a medical device (SaMD), including the development and implementation of a software quality management system. She is particularly interested in integrating longstanding scientific and computing methods with existing workflows and systems to streamline processes and increase efficiency,

Prior to specialising in medical image computing, her academic background was of physics from Trinity College Dublin, where she researched additive manufacturing techniques for magnetic materials and machine learning for data mining of materials synthesis.

Email Riannach.Semple@uhs.nhs.uk

Katja is a biomedical engineer who combines her academic background, including a PhD and postdoctoral work, with industry experience. She specialises in ultrasound and cone beam computed tomography (CBCT) imaging techniques, and has a keen interest in applying deep learning to imaging. As part of the scientific computing team, she oversees and continuously enhances our research imaging data service for clinical trials, which operates on XNAT.

Email ekaterina.zilonova@uhs.nhs.uk

Roberto is a first-year trainee clinical scientist currently undertaking the Scientist Training Programme (STP) specialising in clinical scientific computing. His training involves placements across various departments and teams within UHS (scientific computing, informatics, medical physics and clinical engineering) in the first year, and specialist training in software development and clinical computing systems in the second and third year.

Before enrolling in the STP programme, Roberto completed an integrated Masters in Computer Science with Artificial Intelligence at the University of Southampton, where he worked as part of a multidisciplinary team to develop a wearable device to monitor peripheral oedema for his MEng project, using technologies such as Internet of Things (IoT) and artificial intelligence (AI).

Email roberto.martinezcamacho@uhs.nhs.uk