Abstract:

From mapping the spread of disease to monitoring climate change, data holds the key to solving some of the world’s biggest challenges. Dependable decisions rely on understanding the provenance and reliability of data. Historically, only a small fraction of the generated data was shared and re-used, while the majority of data were used once and then erased or archived. At NPL Data Science we are defining best practice in measurement data reuse and traceability by developing metadata standards and data storage structures to locate and interpret datasets and make them available for sharing, publication and data mining.

The talk will shed light on the most burning issues in the scientific data management, and illustrate it with examples from industrial and academic practices. It will present several NPL Data Science projects that focus on delivering confidence in data obtained from life science imaging, medicine, geosciences and fundamental physics.

Speaker Bio:

Dr Marina Romanchikova joined the NPL Data Science team in 2017 to work on data quality and metadata standards. She obtained an MSc in Medical Informatics at University of Heidelberg, Germany, where she specialised in medical image processing and in management of hospital information systems. In 2010 she received a PhD on Monte Carlo dosimetry for targeted radionuclide therapy at the Institute of Cancer Research in Sutton, UK. Marina worked six years as a radiotherapy research physicist at Cambridge University Hospitals where she developed methods for curation and analysis of medical images.

Current interests

– Quantitative quality assessment of medical images and medical image segmentation
– Harmonisation of medical and healthcare data from heterogeneous sources
– Applications of machine learning in healthcare
– Automated data quality assurance

Abstract:

There has been a dramatic growth in the number and range of Internet of Things (IoT) sensors that generate healthcare data. These sensors stream high-dimensional time series data that must be analysed in order to provide the insights into medical conditions that can improve patient healthcare. This raises both statistical and computational challenges, including where to deploy the streaming data analytics, given that a typical healthcare IoT system will combine a highly diverse set of components with very varied computational characteristics, e.g. sensors, mobile phones and clouds. Different partitionings of the analytics across these components can dramatically affect key factors such as the battery life of the sensors, and the overall performance. In this work we describe a method for automatically partitioning stream processing across a set of components in order to optimise for a range of factors including sensor battery life and communications bandwidth. We illustrate this using our implementation of a statistical model predicting the glucose levels of type II diabetes patients in order to reduce the risk of hyperglycaemia.

Speaker Bios:

Lauren and Peter are final year PhD students at the CDT in Cloud Computing for Big Data at Newcastle University. Peter has a background in Computer Engineering from University of Žilina, Slovakia and a double-degree in Computer Software Engineering from JAMK University of Applied Sciences, Jyväskylä, Finland. His research interests are within distributed event processing, edge computing and Internet of Things with a special focus on energy and bandwidth constrains. Lauren has an MMath degree from Newcastle University and her research interests lie in statistical modelling of time series data.

Abstract:

Apprenticeship degrees have sprung up so fast that there has been little time for us all to reflect on how this apparently new form of education, to universities at least, could significantly affect our educational offerings. The University of Glasgow has been undertaking some preparatory work for Skills Development Scotland prior to running its apprenticeship degree in software engineering, and this has afforded us some time to see what others nationally and internationally have been doing, and to consider relevant aspects of the literature, as well as consult with industry. One view that we are developing of these degrees is as a true evolution of typical, largely campus-based, software engineering degrees, towards a full-blown professional degree such as in medicine, where university and hospitals are in real partnership over the training of doctors. In this talk, I will outline our thinking and raise a number of issues for discussion. In suggesting a closer relationship with industry in a talk in St Andrews, I do not of course miss the irony that industry accreditation was never (I believe) something that St Andrews was particularly bothered about – thinking that my BSc (Hons) 1988 is not accredited!

Abstract:

Tissue ablation is a widely used treatment in both the cosmetic and medical sectors, for treating various diseases or to improve cosmetic outlooks. We present our tissue ablation model which can predict the depth of ablation, and the surrounding thermal damage caused by the laser during ablation.

“Non-diffracting” beams have a multitude of uses in physics, from optical manipulation to improved microscopy light sources. For the first time we show that these beams can be modelled using Monte Carlo radiation transport method. Allowing better insight into how these beams propagate in a turbid medium.

Both of these projects use the Monte Carlo radiation transport method (MCRT) to simulate light transport. The MCRT method is a powerful numerical method that can solve light transport though heavily scattering and absorbing mediums, such as biological tissues. The method is extremely flexible and can model arbitrary geometries and light sources. MCRT can also model the various micro-physics of the simulated medium, such as polarisation, fluorescence, and Raman scattering. This talk will give an overview of our group’s work, with particular focus on simulating tissue ablation, and modelling “non-diffracting” beams.

Speaker Bio:

Lewis McMillan is a final year physics PhD student at St Andrews University. His research interests are in using Monte Carlo radiation transport method for various applications within medicine and biophotonics.

This is joint work with Charlie Blake.

Abstract:

The most famous single-player card game is ‘Klondike’, but our ignorance of its winnability percentage has been called “one of the embarrassments of applied mathematics”. Klondike is just one of many single-player card games, generically called ‘solitaire’ or ‘patience’ games, for which players have long wanted to know how likely a particular game is to be winnable for a random deal. A number of different games have been studied empirically in the academic literature and by non-academic enthusiasts.

Here we show that a single general purpose Artificial Intelligence program, called “Solvitaire”, can be used to determine the winnability percentage of approximately 30 different single-player card games with a 95% confidence interval of ± 0.1% or better. For example, we report the winnability of Klondike to within 0.10% (in the ‘thoughtful’ variant where the player knows the location of all cards). This is a 30-fold reduction in confidence interval, and almost all our results are either entirely new or represent significant improvements on previous knowledge.

Speaker Bio:

Ian Gent is professor of Computer Science at the University of St Andrews. His mother taught him to play patience and herself showed endless patience when he “helped” her by taking complete control of the game. A program to play a patience game was one of the programs he wrote on his 1982 Sinclair Spectrum now on the wall outside his office.

Abstract:

This talk is an overview of the VAMPIRE (Vessel Assessment and Measurement Platform for Images of the REtina) project, an international and interdisciplinary research initiative created and led by the Universities of Dundee and Edinburgh in Scotland, UK, since the early 2000s. VAMPIRE research focuses on the eye as a source of biomarkers for systemic diseases (e.g. cardiovascular, diabetes, dementia) and cognitive decline, as well as on eye-specific diseases. VAMPIRE is highly interdisciplinary, bringing together medical image analysis, machine learning and data analysis, medical research, and data governance and management at scale. The talk introduces concisely the aims, structure and current results of VAMPIRE, the current vision for effective translation to society, and the several non-technical factors complementing technical research needed to achieve effective translation.

Speaker Bio:

Emanuele (Manuel) Trucco, MSc, PhD, FRSA, FIAPR, is the NRP Chair of Computational Vision in Computing, School of Science and Engineering, at the University of Dundee, and an Honorary Clinical Researcher of NHS Tayside. He has been active since 1984 in computer vision, and since 2002 in medical image analysis, publishing more than 270 refereed papers and 2 textbooks, and serving on the organizing or program committee of major international and UK conferences. Manuel is co-director of VAMPIRE (Vessel Assessment and Measurement Platform for Images of the Retina), an international research initiative led by the Universities of Dundee and Edinburgh (co-director Dr Tom MacGillivray), and part of the UK Biobank Eye andVision Consortium. VAMPIRE develops software tools for efficient data and image analysis with a focus on multi-modal retinal images. VAMPIRE has been used in UK and international biomarker studies on cardiovascular risk, stroke, dementia, diabetes and complications, cognitive performance, neurodegenerative diseases, and genetics.