(Clockwise from top left): Dr Ryan Ames, Dr Fabrizio Costa, Dr Charlotte Tupman and Dr Jacq Christmas

IDSAI Research Awards: tackling challenges in the Humanities and Life Sciences through Data Science and AI

Two research projects have been selected to receive the first IDSAI Research Awards.

Congratulations to Dr Charlotte Tupman, Dr Jacq Christmas, Dr Ryan Ames and Dr Fabrizio Costa, whose applications in the first round of IDSAI Research Awards have been successful.  IDSAI Research Awards 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 were invited to apply for the time of an IDSAI Research Fellow to work with them on a pilot research project.  Following a competitive application process the following two projects were successful:

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

IDSAI Research Fellow: Dmitry Kangin

This pilot project will examine on an unprecedented scale the planning processes of ancient Latin inscriptions, one of our major sources of evidence for the Roman world. It will develop 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, Research Fellow in Computer Science, will work with Tupman and 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 Kangin will develop a new basis for training Neural ODEs (Ordinary Differential Equations). By combining machine learning and traditional epigraphic methods, the project aims to deepen our understanding of Roman drafting and stonecutting processes, and ultimately to enhance our ability to make accurate restorations of fragmentary inscriptions.

A machine learning approach to predicting infection rate in the fungal plant pathogen Magnaporthe oryzae
Ryan Ames (Biosciences) and Fabrizio Costa (Computer Science)

IDSAI Research Fellow: Oliver Stoner

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.

Our project aims 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.

The next round of IDSAI Research Awards is due to open in late January 2020.  Please look out for more information about the Awards here. 

Date: 2 October 2019