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IDSAI Funded Pilot Projects - Past Awards

IDSAI Research Awards and Turing-Exeter Pilot Research Grants aim to support early-phase development of ideas for innovative, interdisciplinary data science and AI research across Exeter.  Academic members of staff from all Colleges can apply for the time of an IDSAI Research Fellow to work with them on a pilot research project. 

You can read about each of the projects from previous rounds of awards below and if you would like to discuss the outcomes of the projects please contact the PIs. 

For further information about IDSAI research awards or Exeter-Turing Pilot Grants please contact:

Reconsidering the Roman Workshop: examining the processes behind the making of inscribed texts
Collaborators: Charlotte Tupman (Classics and Ancient History / Digital Humanities) and Jacqueline Christmas (Computer Science)

IDSAI Research Fellow: Dmitry Kangin

Description: This pilot project examined on an unprecedented scale the planning processes of ancient Latin inscriptions, one of our major sources of evidence for the Roman world. It developed a machine learning model to locate and extract characters and make a series of measurements to establish the extent to which Roman workshops used specific controlling ‘modules’ in the creation of inscribed texts.

Dr. Dmitry Kangin, IDSAI Research Fellow in Computer Science, worked with Charlotte Tupman and Jacqueline Christmas to apply neural networks to analyse a subset of the almost 40,000 images that have been made available by the Epigraphische Datenbank Heidelberg. As part of his work Dmitry developed a new basis for training Neural ODEs (Ordinary Differential Equations). By combining machine learning and traditional epigraphic methods, the project deepened understanding of Roman drafting and stonecutting processes, and ultimately enhanced the ability to make accurate restorations of fragmentary inscriptions.

A machine learning approach to predicting infection rate in the fungal plant pathogen
Magnaporthe oryzae

Collaborators: Ryan Ames (Biosciences) and Fabrizio Costa (Computer Science)

IDSAI Research Fellow: Oliver Stoner

Description: Global demand for food is rising and crop production needs to increase in order to provide for a human population expected to be greater than 9 billion by the year 2050. Issues such as water crises, climate change, pests and pathogens can cause significant crop losses and represent major challenges for global food security. In particular, pests and pathogens account for yield losses of up to 20% of the world's harvest each year, with a further 10% loss post-harvest. Our project focusses on fungi, the most important plant pathogens that cause the loss of 125 million tonnes of crops each year. The spread of fungal plant pathogens and the ineffectiveness of available fungicides pose serious threats to global food security.

This project aimed to apply machine learning methods to biological data in order to predict whether fungi will be able to infect different varieties of crops. Being able to accurately predict infection will enable us to forecast food security threats and inform policies that protect crops. Moreover, these methods have the advantage of identifying the key genes, and interactions between genes, that predict infection. These genes therefore, represent potential targets for the next generation of fungicides.

Assessing the risks of harmful algal blooms and impact on marine aqua culture
Collaborators: Dr Theo Economou (Mathematics); Dr Ross Brown (Bioscience), Dr Ian Ashton (Renewable Energy); Dr Fabrizio Costa (Computer Science); Dr Ricardo Torres (Plymouth Marine Laboratory).

IDSAI Research Fellow: Oliver Stoner

Description: Aquaculture is the fastest growing food production sector globally and is vital for future global food security. SW England is a prime area for the expansion of shellfish farming. However, the uptake of biotoxins from naturally occurring Harmful Algal Blooms (HABs) into shellfish is a significant problem. The main risk is intoxication of human consumers, which causes various shellfish poisoning syndromes (from stomach upsets, to amnesia and paralysis).

The vision for this pilot research project was to make a step change in the accuracy of HAB risk prediction both spatially and temporally, thus facilitating operational and development planning for the UK (and wider) mariculture industry. Development of statistical models for predicting HAB risk and impacts on mariculture will have major academic and societal impact. Importantly this project provided pilot data and key learning for the future development of mechanistic models that will be applicable over wider temporal and spatial scales, including future climate change scenarios.

Who Benefits in Policy Trials?
Collaborators: Dr ZhiMin Xiao (Graduate School of Education) and Professor Mihaela van der Schaar (John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine, University of Cambridge & Turing Fellow).

IDSAI Research Fellow: Dmitry Kangin

Description: The use of large-scale randomised controlled trials (RCTs) are fast becoming “the gold standard” of testing the causal effects of policy interventions. RCTs are typically evaluated by the statistical, educational, and socioeconomic significance of the average treatment effect (ATE) on the study sample. Interventions that do not have a statistically significant ATE are often discarded as not meaningful and, as a result, usually not implemented more widely, partly due to the failure to recognise the difference between statistical and substantive hypotheses. However, while some interventions may not have an effect on average, they might still have a meaningful effect on a relevant subgroup of individuals, for whom the treatment is beneficial. In some cases, an intervention could thus still provide a net benefit if rolled out. Understanding and identifying for whom a treatment works is therefore critical for policy-makers and society at large.

Recognising the strengths of RCTs as a research and evaluation tool this project proposed an individualised approach to impact estimation. Thanks to recent development in machine learning and the increase in both quantity and quality of research data, it makes greater sense than before to estimate and compare treatment effects at individual and group levels, ultimately generating deeper insights into the causal treatment effects of tested interventions by uncovering what worked, for whom, and by how much. As in any research, the individualised approach, has its limitations. This project looked to validate the individualised approach using both simulations and empirical studies to help practitioners better understand the cutting-edge influence function based approach to the evaluation of machine learning algorithms for ITEs without the need to access counter-factual outcomes.

Multimodel Techniques for the Study of Sexism in Political Institutions

COLLABORATORS: Professor Susan Banducci (QStep, SSIS, Turing Fellow), Prof. Hilde Coffé (University of Bath, Department of Politics, Languages and International Studies), Dr. Tom Fincham Haines (University of Bath, Computer Science).

IDSAI Research Fellow: Dr Ravi Pandit

Description: Sarah Child’s “Good Parliament” Report of 2016, which focused on making the UK House of Commons more inclusive especially for women, first recommendation was that “unprofessional, sexist and exclusionary language and behaviour should have no place in the House”. This sexist behaviour creates a barrier to the full participation of women in policymaking and decreases democratic quality. Therefore, it is crucial to study when, where and how sexism occurs in political institutions.   Given that sexism is not one behavioural trait but can be exhibited in many ways (including through voice and gestures), the aim of this project is to explore the feasibility of developing a multimodal technique (relying on text, image and audio) to measure sexist language and behaviour in the House of Commons.

A machine-learning approach to classifying neuron type

COLLABORATORS: Dr Akshay Bhinge (College of Medicine and Health), Dr David Richards (Physics), Professor Krasimira Tsaneva-Atanasova (Mathematics, Turing Fellow), Dr Kate Madden (University of Newcastle).

IDSAI Research Fellow: Dr Ravi Pandit

Description: Motor neuron disease (MND) is a devastating neurodegenerative condition characterized by loss of motor neurons. Understanding the molecular events that lead to motor neuron degeneration in MND is paramount in developing therapies to halt or even reverse the degeneration. Monitoring of motor neuron health in vitro is currently performed using immunostaining where cells are chemically fixed and specific intracellular proteins are stained with fluorescent antibodies. However, immunostaining does not allow real-time monitoring of motor neuron health and can lead to artefacts in neuronal structure. Real-time analysis is important because MND motor neurons display progressive deterioration over extended time periods in vitro. What is critically needed is the ability to identify motor neurons in unstained images. At the moment, however, this is simply not possible. In this project, the team aim to rectify this by developing a novel machine learning approach that can identify neuron type based solely on cell shape without the need for any staining.

AI-enhanced decision support systems for treescapes in the UK

COLLABORATORS: Daniel Williamson (CEMPS, Turing Fellow), Brett Day (Business School), Ian Bateman (Business School), Deyu Ming and Serge Guillas (UCL,Turing)

IDSAI Research Fellow: Dr Bertrand Nortier

Description: The 2019 Climate Change Act amendment requires the UK to achieve net zero greenhouse gas (GHG) emissions by 2050. Crucial to any strategy for meeting this target is the widespread planting of trees, removing GHG from the atmosphere. Design of “treescaping” policies must address two challenges: (i) A variety of factors (such as reliance upon private-sector uptake of incentives) introduce uncertainty regarding the level of GHG removed by tree planting; (ii) Alongside carbon sequestration, tree planting affects a wide array of other ecosystem services including biodiversity, flood, recreation, fire risk, food production and so on.

For each element of (i) and (ii) different research communities have models for their piece of the process. All models are interconnected and can be computationally intensive. To provide policy support, the project aims to run this network of models, propagating their uncertainties over a continuum of policies, in order to present decision makers (DM) with a subspace of policies that are consistent with their stated targets for CO2 sequestration and illustrate trade-offs with costs and other ecosystem services. DM may have an ill-formed prior understanding regarding the trade-offs they might accept across impacts, so rather than using a multi-objective optimisation, the vision is to deliver a mapping of the whole policy space for DMs to explore in real time, offering the potential for radical improvements in policy.

Identifying drug-vaccine interactions in electronic health record data

COLLABORATORS:  Prof William Henley (Health Statistics Group, CMH), Prof Mark Kelson (CEMPS; Turing Fellow), Prof Sebastian Vollmer (University of Warwick; Turing Institute), Dr John Dennis (CMH), Dr Lauren Rodgers (CMH), Dr Adam Streeter (Mùˆnster Universitätklinikum), Dr Gus Hamilton (NIHR Academic Clinical Fellow, North Bristol NHS Trust).

IDSAI Research Fellow: Dr Oliver Stoner

Description:  As progress is made towards developing vaccines against COVID-19, there is increasing interest in understanding ways of enhancing the effectiveness and safety of both new and existing vaccines. Understanding ways of enhancing vaccine response has the potential to have a major impact on tackling morbidity and mortality from existing and emerging infectious diseases in vulnerable populations.

Some of the most common prescription drugs in the UK are medications to treat high blood pressure, statins to treat or delay cardiovascular disease, proton pump inhibitors for heart-burn and acid related disorders, metformin to delay or treat diabetes and non-steroidal anti-inflammatory drugs.  A number of these medications are also known to have modulating effects on the immune system that could either inhibit or enhance the immune response when patients receive routine vaccinations. The project team aims to use machine learning approaches to facilitate a data-driven exploration of drug-vaccine interactions to identify candidates for future investigation. This data-intensive screening approach has the potential for rapid deployment at scale to validate interactions with known mechanisms as well as contributing to the discovery of new targets. This proposed project combines methodology development with delivering clinically-relevant results for influenza vaccination.

Transfer learning for clinical applications

COLLABORATORS:  Prof David Llewellyn (CMH and Turing Fellow),  Dr Neil Oxtoby (UCL), Dr Janice Ranson (CMH), Dr Charlotte James (CMH), Razvan Marinescu, (Postdoctoral Researcher in Medical Image Computing, MIT)

IDSAI Research Fellow: Dr Bertrand Nortier

Description:  One barrier to the development and implementation of robust unbiased algorithms is the heterogeneous nature of clinical, population-based and experimental data. Attempts to enhance healthcare have been hampered by the use of single datasets to develop algorithms that are overfitted and don’t perform well in other datasets, contexts or populations. Models developed for rare conditions are particularly problematic due to limited training data and opportunities for external validation. Transfer learning may be a solution to these difficulties, allowing for knowledge learnt from one dataset to be transferred to a second, where the data is similar. The primary objective of the project is to investigate the utility of transfer learning for clinical application. The project will assess whether a machine learning model trained to predict two-year dementia risk can be transferred between clinical samples. As a secondary objective the project will evaluate the utility of Disease Knowledge Transfer ( to extend the way in which transfer learning can be used with clinical data.

Automated Detection of Hillforts in South West England

COLLABORATORS:  Dr João Fonte (Co-I) and Dr Ioana Oltean (Archaeology), Professor Leif Isaksen (Digital Humanities) Dr Jacqueline Christmas (Computer Science) (Co-I); Dr Albert Chen (Centre for Water Systems);  Jane Galwey (Camborne School of Mines)

IDSAI Research Fellow: Dr Dmitry Kangin

Description:  This project uses a machine learning approach to test the automated pattern recognition of, and data enhancement for Iron Age hillforts in South West England based on airborne laser scanning data.

Despite the recognition of their great potential the development of automatic detection methods applied to archaeological remote sensing is still in its infancy and limited to very simple morphologies. However, the increasing availability of remote sensing data of higher resolution, higher acquisition frequency and lower cost, demands a paradigm shift, where traditional man-made identification and mapping of archaeological sites cannot be the only option available.

The project will test the extent to which existing algorithms developed for other purposes can assist the detection in airborne LiDAR datasets of hillforts.  The project proposes a meaningful solution to protect archaeological heritage and fight the rate of its destruction due primarily to development and climate change globally. If successful, this approach could be expanded and applied to other case studies involving similar archaeological objects elsewhere; other types of archaeological features; potentially other types of datasets (e.g. multispectral aerial and satellite imagery). This will be highly relevant in particular for cultural heritage management by national and local agencies in Britain and beyond.

Can automated image analysis techniques drive a revolution in our understanding of climate change?
COLLABORATORS: David Reynolds (CGES), Paul Halloran (Geography), Bryan Black (University of Arizona)
IDSAI Research Fellow: George De Ath

Description: Growth rings formed in trees, clam shells, corals and fish provide unique records of the environmental conditions at the time the ring was formed. Given the great age that these species can reach, these growth ring records can provide long-term perspectives on modern climate variability. These records are ultimately used to help improve the accuracy of climate models used for predicting future climate change. However, the process of developing these records is painfully slow as each ring is manually measured and validated using statistical techniques. Whilst advances have been made in the software used to perform these analyses, significant potential remains for optimising the methodology and improving our understanding of uncertainties within these data. This project seeks to explore the potential of using automated image analysis techniques and Bayesian statistics to automate the analysis of growth rings formed in trees and clams. It is hoped that these advanced techniques will speed up the development of new long-term environmental records whilst preserving the statistical rigour with which these records are built. These new and extended records will help further our understanding of how the oceans and atmosphere change through time and ultimately improve the accuracy of future climate forecasts. 

Automated identification of protein filaments in high-resolution 3D image data
COLLABORATORS: Dr Vicki Gold (CLES) and Dr Danielle Paul (Turing Fellow, University of Bristol)
IDSAI Research Fellow: Dmitry Kangin
Description: Cryo-electron tomography (cryoET) is a specialised 3D high-resolution imaging method used to visualise protein complexes and determine their structures. We image protein filaments, which are long hair-like assemblies that play important roles in a plethora of cellular processes. Examples include actin and myosin found in muscle, as well as pilins/flagellins which drive microbial motility.  By averaging many thousands of copies of protein filaments together from our 3D data, we can determine high-resolution protein structures at atomic-level detail (Fig. 1). Such structures help to reveal fundamental insight into their function, which provides key knowledge for healthcare and drug discovery programmes. A major limitation of our work is filament identification and selection for downstream processing, which is mostly a manual process. In this proposal, we aim to establish the feasibility of automated filament identification in cryoET data using two- and three-dimensional convolutional neural networks. Removing this restrictive bottleneck will change the way in which cryoET imaging data can be processed, increasing efficiency and increasing the accessibility of our method to the scientific community.

Inferring variation in the meanting of political terms over time
COLLABORATORS: Dr Chico Camargo, CEMPS, University of Exeter, affiliated academic at IDSAI, Co-I: Dr Barbara McGillivray, Turing Research Fellow, The Alan Turing Institute, Senior Research
IDSAI Research Fellow: Dr Bertrand Nortier
Description: Language is an important force shaping politics and guiding collective sensemaking. This is evident with the rise of terms such as "gender pay gap", "Brexit" even "lockdown". In this context, lexical semantic change, i.e. the change in meaning of words, is fast, wide, and weaponised. This makes it pressing to ask: can we detect and measure how meanings change or persist in certain communities? Can we track lexical semantic change at scale, in real time?

This project will develop new data science methods to track the semantic change of political terms. It will combine tools from natural language processing, machine learning, and network science, to make "socially aware" computational models of lexical semantic change. We will do so by extending methods of semantic change detection based on word embeddings, as well as token and sense embeddings, applying them to a series of political terms present in debates in the UK Parliament over the last decade, and incorporating metadata on party affiliation, debate topic, and debate participants, to reveal how word meanings were shared – and built – across different parties and debates. As such, this project will enable us to measure the dynamics of semantic divergence and convergence, as well as coordinated behaviour in the UK Parliament.

Spinal muscle segmentation and characterisation
COLLABORATORS: Jude Meakin (Biomedical Physics), Jonathan Fulford (Medical Imaging), Karen Knapp (Medical Imaging), Greg Slabaugh (Computer Science, QMUL)
IDSAI Research Fellow: Dr Bertrand Nortier
Description: Our project aims to develop computer software that can automatically assess spinal muscle size and quality from medical images. Our spine muscles are essential for controlling the movement of our upper body, allowing us to stand upright, adopt a range of postures, and lift objects from the ground. These muscles, however, are often smaller and fattier in people who have conditions such as back pain and osteoporosis. Smaller and fattier muscles indicate that the muscle has become weaker, making it less able to control the spine. However, we do not know whether muscle weakness is a symptom or a cause of these conditions. Large scale studies are required to answer this question, and these studies need suitable methods for quickly and accurately assessing muscle size and quality. Therefore, our project aims to develop computer software that can identify the spinal muscles in magnetic resonance images and then automatically calculate the muscle size and the amount and distribution of the fat within the muscle. Once we have developed our software, we will use it on images we already obtained to determine how muscle size and fat varies in people with and without osteoporosis.

Social Sensing of Volcanic Crises
COLLABORATORS: Dr James Hickey (CSM), Prof. Hywel Williams (Alan Turing Fellow & Comp. Sci.) & Dr Rudy Arthur (Comp. Sci.); Co-I = Michelle Spruce (Comp. Sci.); External Collaborator: USGS
IDSAI Research Fellow: Dr Ravi Pandit
Description: Volcanic crises cause significant damage, casualties and economic loss. Social sensing is the systematic analysis of unsolicited social media data to observe real world events. Our project aim is to use social sensing to analyse the social and economic impact of volcanic crises through space and time, and the change in emotional response to a crisis as it unfolds, from pre-eruption warnings through to post-eruption recovery. Current understanding of social impacts of volcanoes are poorly understood and there is no widely-accepted best practice in communicating volcano hazard information; our proposed analyses will contribute to solving these problems. We will initially focus on the 2018 Kilauea eruption in Hawaii, USA, in collaboration with the United States Geological Survey (USGS), before looking at volcanic crises from a more global standpoint. By developing a set of tools to socially sense the economic and social impact of volcanic hazards, and societal reaction to official hazard management communications, we will help define best-practise in this area to benefit the 10% of the world’s population living with volcanic hazards.

Understanding right-wing politics and leadership through linguistic style
COLLABORATORS: Dr Miriam Koschate-Reis, Senior Lecturer Social Psychology, Psychology (CLES), Dr Travis Coan, Senior Lecturer Politics, Politics (CSSIS), Prof Susan Banducci, Politics (CSSIS) and Turing Fellow, Postdoctoral collaborators: Dr Elahe Naserian-Hanzaei, Politics (CSSIS), Ms Alicia Cork, Psychology (Bath)
IDSAI Research Fellow: Ravi Pandit
Description: One of the most fundamental questions in leadership research is whether leaders shape groups (identity entrepreneurship), or whether changes to a group’s self-understanding require a change in leadership (leader prototypicality). The recent Trump presidency illustrates this question well: On the one hand, political analysts have suggested that Trump was elected leader in response to shifts in the Republican Party created by the Tea Party movement. On the other hand, it has been proposed that Trump has shaped “American Nationalism” as a movement and embedded it in the Republican party. However, to the best of our knowledge, there is no research examining prototypical leadership and identity entrepreneurship together over time by examining changes to a group’s self-understanding, and the fit of leaders to this self-understanding. This is a particularly important question given the rise of extreme right-wing groups across the world, and their public endorsement by “mainstream” political leaders. 

Our recent research suggests that a group’s self-understanding is reflected in the linguistic style of its members. By analysing online forum data of right-wing groups and speeches of members of the U.S. congress (leaders), this project aims to examine the self-understanding of a wide spectrum of right-wing and conservative political groups in the U.S., and the role that leadership plays in this self-understanding. Combining computational and statistical methods with political and psychological theory will allow us to gain novel insights into (1.) the dominant group types found in right-wing U.S. politics, and whether there is a differentiation in self-understanding between mainstream and right-wing groups, (2.) the extent to which leaders drive changes in the self-understanding of their group, or whether changes in the group’s self-understanding require a change in leadership.