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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: idsai@exeter.ac.uk

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.