[03/07/23] Bayes Centre News: Discover the Edinburgh Students Joining the Turing Student Enrichment Scheme
Four students from the University of Edinburgh in the School of Engineering and School of Informatics have joined this year's Alan Turing Institute Student Enrichment Scheme.
The Bayes Centre was one of the founders of the Alan Turing Institute and is a member of the Turing University Network. We take a proactive role in coordinating activities across the University, providing a focus for academic data science and AI research and connectivity to the Institute’s UK-wide community.
The Enrichment scheme is now in its 8th year and is designed to give students the opportunity to enhance, refresh, and broaden their research with the Turing’s community and in recognition of their place within the UK’s growing data science and AI research community.
We are delighted to announce that 4 University of Edinburgh's students have successfully made it onto this year's Enrichment scheme.
Enrichment students have the opportunity to find new collaborators for their research or related work. Enrichment Awards allow students to join a cohort from across the UK, as well as the range of researchers already active at the Turing.
Discover the students who will be joining this year's Alan Turing Enrichment Scheme.
Bryan is a PhD student in the Biomedical AI CDT program at the University of Edinburgh, under the supervision of Dr. Arno Onken and Dr. Nathalie Rochefort. His main research focuses on modelling neuronal activities recorded from the visual cortex using deep learning methods and tries to make sense of how neural responses reshape over experience. As the capability of recording large amounts of neural and behavioural data expands, there is increasing interest in modelling neural dynamics during adaptive behaviours in order to understand neuronal encoding of sensory and motor information.
A popular approach to identify the underlying computations of the visual system is by building high-performing predictive models that can accurately predict neural responses to naturally occurring stimuli. These models can explain a large part of the stimulus-driven variability and account for the nonlinear response properties of the neural activity, thus allowing computational neuroscientists to generate new hypotheses about biological vision and bridge the gap between biological and computer vision. To that end, Bryan’s research project aims to utilise the latest advancement in deep learning to design and build these biological digital twins and join behavioural and neural data to reveal neural dynamics.
Profile page: https://bryanli.io
Lara Johnson is a second year PhD student at the Advanced Care Research Centre (School of Engineering) supervised by Dr Sohan Seth (Informatics), Dr Atul Anand (Centre for Cardiovascular Sciences) and Dr Alan Marshall (SPS). Her research – which integrates the fields of data science and geriatric medicine - relates to the Turing’s research challenge of revolutionising healthcare through maximising the information in patient health records and increasing collaboration between data scientists and medical researchers.
Lara is using statistical and machine learning methods on patient health records and survey data to study how combinations of health issues affect older people’s risk of falls, fractures, hospital admissions and death. The data she works with is largely binary and can take on only two possible states (each health issue can only be present or absent). Moreover, it is generally sparse (most health issues are present in only a minority of people). Being able to turn binary data into useful information is important for making use of health records. During her time at the Turing, Lara will work on the methodological challenges in applying machine learning techniques to binary and sparse data.
I am a second year PhD student at The University of Edinburgh co-supervised by Dr Javier Escudero Rodriguez, Dr Chen Qin and Dr Milly Lo. I received a BSc in Computer Engineering and Information Technology from University of Dar es Salaam, Tanzania and an MSc degree in Artificial Intelligence from University of Edinburgh, UK.
In my research, I aim to advance healthcare delivery in the ICU by leveraging machine learning tools to aid medical researchers in analysing routinely collected physiological data at the bedside. During the first year of my PhD, I focused on investigating and developing unsupervised machine learning techniques for detecting noise in the data. This work directly tackles the laborious and expensive manual data cleaning process that experienced researchers traditionally perform before utilizing the data for medical research purposes.
Looking ahead, I plan to make the most of my time at the Turing Institute by further exploring and developing interpretable machine learning models. This strategic direction is crucial in overcoming one of the main barriers hindering the widespread adoption of machine learning tools in healthcare: the lack of interpretability. By enhancing the interpretability of these models, I aim to facilitate their integration and acceptance within the healthcare domain, where transparency and understanding play vital roles.
In a world with a fast-growing and rapidly aging population, where availability and accuracy of diagnosis is key to early detection and treatment of disease and injury, the development of enhanced medical imaging techniques will improve the wellbeing of unwell members of society. In collaboration with Adaptix Imaging, I am developing a core component for a portable system for 3D X-ray imaging that will combine the superior clinical diagnosis of 3D with the lower running costs and radiation doses of traditional 2D radiography.
The Alan Turing Enrichment Scheme will augment my PhD programme by allowing me to develop machine learning algorithms to predict the thermal failure of field emitters, a serious reliability issue that is slowing down the commercialisation of 3D X-ray imaging systems. The outcome of this research is new knowledge that contributes towards the development of low dose, low cost and mobile 3D medical imaging devices.