Exeter Turing Fellows

Professor Michael Allen

I have lived a varied life in medical and healthcare research. I am pharmacologist by background and spent 20 years in Pharma in identifying new medicines (some of them work; others, well....). As part of that I had to learn to apply simulation techniques to understand why our research was so slow. That led on to using simulation to understand why healthcare systems are often so slow! And recently we have had breakthroughs in machine-learning, which allow us to model systems (either biological or organisational) in a new level of detail. We are using this this to model clinical decision-making in emergency stroke care - understanding how different hospitals might make different decisions about the same person (see https://samuel-book.github.io/samuel-1/). From the age of 14, a long long time ago, I have always been passionate about the use of computers. I loved the early open days of people sharing code, and then watched in dismay at how computer technology, even computer languages, became closed and 'protected' by IP. So I am now thrilled to see how much it has opened up again - from Ebon Upton's Raspberry Pi to Linux, open neural network libraries like TensorFlow, Keras, and PyTorch, and The Turing Way. My Ph.D. is now 30 years behind me, but I am happy to say that I still really love the fun and joy of research, and how computers constantly allow us to be pushing further. Come and chat if you just want to have a chin-wag and gossip about what may be lying just behind the boundary of what we can see for sure.

Prof Edmond Awad

Edmond Awad is a Lecturer (Assistant Professor) in the Department of Economics and the Institute for Data Science and Artificial Intelligence at the University of Exeter. He is also an Associate Research Scientist at the Max Planck Institute for Human Development, and is a Founding Editorial Board member of the AI and Ethics Journal, published by Springer. Before joining the University of Exeter, Edmond was a Postdoctoral Associate at MIT Media Lab (2017-2019). In 2016, Edmond led the design and development of Moral Machine, a website that gathers human decisions on moral dilemmas faced by driverless cars. The website has been visited by over 4 million users, who contributed their judgements on 70 million dilemmas. Another website that Edmond co-created, called MyGoodness, collected judgements over 2 million charity dilemmas. Edmond’s work appeared in major academic journals, including Nature, PNAS, and Nature Human Behaviour, and it has been covered in major media outlets including The Associated Press, The New York Times, The Washington Post, Der Spiegel, Le Monde and El Pais. Edmond has a bachelor degree (2007) in Informatics Engineering from Tishreen University (Syria), a master’s degree (2011) in Computing and Information Science and a PhD (2015) in Argumentation and Multi-agent systems from Masdar Institute (now Khalifa University; UAE), and a master’s degree (2017) in Media Arts and Sciences from MIT. Edmond’s research interests are in the areas of AI, Ethics, Computational Social Science and Multi-agent Systems.

Professor Susan Banducci

Susan Banducci, Professor in Politics, is an interdisciplinary researcher working at the interface of computational methods and the social sciences. She has led on developing training programmes for early career researchers in key methodologies (advanced quantitative & computational social science methods). She is PI on TWICEASGOOD - an ERC Advanced Grant (2022-2027, €2.5m) that examines women candidates’ experience of sexism on the campaign trail.

Dr Robert Beardmore

Dr Beardmore held appointments in Mathematics Departments at Bristol University and Imperial College and worked on rigorous problems in dynamics before starting a systems biology lab at Exeter University in 2010. It became apparent at that time that ideas from control theory and optimisation could help build intuition on how antibiotic resistance might be controlled in clinical settings. So, using whole-genome data isolated from patients allied to mathematical modelling techniques and using new laboratory assays inspired by the mathematics, my lab set about showing this by forcibly evolve antibiotic resistance ‘de novo’ in bacteria, sometimes using bespoke hardware technologies. Genomic paths to resistance were then compared to clinical datasets, both large and small, where the hope is that data driven techniques will shed light on bacterial weaknesses which are often framed in terms of 'evolutionary tradeoffs’. For example, a tradeoff might see a gain in resistance to one drug causing susceptibility to another drug and we now study open datasets and generate our own data that can help expose such weaknesses. I am particularly interested in detecting copy number variation in clinical microbes where the number of antibiotic resistance genes or plasmids increases in response to treatment. Designing new technologies to classify viral phage is likely to become an important topic in my work in the very near future.

Dr Federico Botta

Federico’s research aims to provide a deeper understanding of human behaviour in urban environments, both at the collective and individual level, by using novel data streams, such as those generated with our interactions with large technological systems, including the Internet and the mobile phone network. These data can also be collected through our usage of smart phone apps and tracking sensors. Federico uses tools from data science, network theory, behavioural and computational social sciences to analyse these data sets and investigate different aspects of human behaviour, and how we can better understand the cities in which we live.

Dr Lewys Brace

Dr Lewys Brace is a Lecturer in Data Analysis in the Q-Step Centre and the SPA department. He is also part of the Policing and Evidence Group and the theme lead for the Institute for Data Science and Artificial Intelligence’s Security and Policing theme. Lewys joined the University of Exeter in 2018 after receiving a PhD in Computational Research Methods from the University of Southampton.
Lewys’ research focuses on online extremism and radicalisation to violent actions, as well as the development of computational research methods for the social sciences. He has led a number of research projects in these areas, and works closes with organisation such as CREST.

Professor Alan Brown

Alan is a university professor, researcher, coach and trusted adviser to start-ups and established organizations across the public and private sector. His work is focused on leading agile approaches to business transformation and understanding the relationship between technology innovation and business innovation in today’s rapidly evolving digital economy. He is founder and director at Unities, a strategic advisory group in digital transformation, and holds a Professorship in Digital Economy at the University of Exeter Business School where he is Director of the DIGIT Lab, a UK National Research Centre in Digital Economy.
Previously, Alan spent over 2 decades in commercial high-tech companies in the USA and across Europe where he led R&D teams, built leading-edge solutions, and guided innovation activities in large scale software product delivery. Alan holds a PhD in Computer Science and is a Fellow of the British Computer Society. In 2019 he also received a Fellowship from the Alan Turing Institute, the UK national institute for AI & data science.

Professor Peter Challenor

Peter Challenor is a professor in the Mathematics Department and head of the Statistics and Data science group. His research interests are in the use of decision making under uncertainty in complex situations. He is particularly interested in uncertainty quantification in complex numerical models. He leads an EPSRC project on uncertainty quantification for exascale computing and is a co-I for the EPSRC Hub for Quantitive Modelling in Healthcare. He sits on the EPSRC Mathematics Strategic Advisory Team and is a member of the advisory board for the Newton Gateway, the impact part of the Isaac Newton Institute for Mathematical Sciences.

Professor Albert Chen

I am an Associate Professor at the Centre for Water Systems (CWS), University of Exeter with over 20 years of experience in Water and Human Environments.
My research vision is unleashing the power of hydroinformatics, enabling efficient and effective solutions to systematically strengthen the resilience of human and environment to the impacts of water-related challenges, for the present and the future climate scenarios.
I am enthusiastic in building digital solutions such that I have developed a series of tools and techniques, including physical modelling, machine learning, data analytics, and high performance computing, to analyse interrelationships between different phenomena, services, and consequences related to water, human and environment. The research has enhanced our ability to predict the behaviours of water and its interactions with natural and human environments. My work has helped international stakeholders involving in different water management practices to determine adequate strategies and measures to better manage the valuable resources for sustainable development.
I am also a UK Senior Expert in the NERC Digital Environment Expert Network, a part of the NERC/UKRI Constructing a Digital Environment (CDE) Programme, to develop the thinking and practice around a ‘digitally enabled environment’, providing benefits for policy makers, businesses, communities and individuals.

Dr Riccardo Di Clemente

Dr. Riccardo Di Clemente is a Lecturer in Data Science at University of Exeter in the department of Computer Science, and Alan Turing Fellow.
The aim of his research is to understand and quantify, using mobile phone data, the relationship between the features of human mobility and the social interactions which lead to the socio-economic structures observed in cities.
As a researcher, Riccardo career has focused on the development of critical multidisciplinary approaches to socio-economic data analysis. His research extends beyond academic outcomes and aims to impact the mainstream – using new tools and big data analysis to enable optimally-informed decisions in the realms of public policy-making and business.

Dr Anjan Dutta

Dr Anjan Dutta is a lecturer of Computer Science at the University of Exeter. He received a PhD in Computer Science from the Autonomous University of Barcelona (UAB) in 2014, which was awarded with an Excellent Cum Laude (highest grade) qualification with International mention. Moreover, he is a recipient of the Extraordinary PhD Thesis Award for the year 2013-14 by the UAB. Before his PhD, he obtained MS in Computer Vision and Artificial Intelligence also from the UAB, MCA in Computer Applications from the West Bengal University of Technology and a BS in Mathematics (Honors) from the University of Calcutta respectively in the year of 2010, 2009 and 2006. After completing his PhD, Dr Dutta worked as a postdoctoral researcher at a few academic institutes including Télécom ParisTech, France; Indian Statistical Institute, India; Computer Vision Center, Spain, among those, the prestigious Marie-Curie postdoctoral fellowship from 2017 to 2019 is to be mentioned. His main research interest focuses on Computer Vision and Machine Learning, specifically on graphic recognition, deep multi-view representation learning, low-shot learning, structural representation learning etc. He has been serving as a technical program committee member/reviewer of many important conferences, such as CVPR, ICCV, BMVC, ICLR, ICML, NeurIPS etc. He has also served as an Area Chair for BMVC 2021. For his work as reviewer, he was enlisted in the top 10% pool of reviewers list of NeurIPS 2020 and in the list of outstanding reviewers of CVPR 2021. He is also a regular reviewer for many prestigious international journals, such as IEEE TPAMI, IEEE TNNLS, IEEE TCYB, IJCV, CVIU, PR etc. He is also an associate editor of the Springer Nature Computer Science (SNCS) journal. He is a member of IEEE, IAPR, CVF, BMVA. He is also a Fellow of Higher Education Academy and the Alan Turing Institute in the UK.

Professor Jonathan Fieldsend

Jonathan Fieldsend graduated with a degree in Economics from Durham University in 1998, an MSc in Computational Intelligence from the University of Plymouth in 1999 and a PhD in Computer Science from the University of Exeter in 2003. His PhD work included developing evolutionary multi-objective optimisation algorithms, and their application to neural network architecture configuration and learning. This was followed by postdoctoral research in machine learning, including Bayesian averaging approaches. His current research interests include multi-objective optimisation; machine learning; optimisation under uncertainty; robust optimisation; multi-modal optimisation; and landscape analysis. This includes the development of both algorithms and specialised data-structures for these tasks. He works closely with partners on application domains spanning expensive data-driven optimisation of engineering design problems through to calibration of digital twins and automated experimental design.

Professor Guangtao Fu

Professor Guangtao Fu is a professor of water intelligence at the University of Exeter. His key areas of expertise cover water resources, urban water and wastewater management, and the interdisciplinary field of artificial intelligence and water engineering. He has extensive experience in developing computer models, data analytics and artificial intelligence algorithms to tackle water challenges with a focus on water security, flood risk and resilience, anomaly detection, urban green infrastructure, and integrated control of urban wastewater systems. He has authored over 150 refereed journal and conference papers and received a number of best paper awards. He is a fellow of the International Water Association (IWA).

Professor Oliver Hauser

Oliver Hauser is an Associate Professor of Economics in the Department of Economics at the University of Exeter Business School, UKRI Future Leaders Fellow leading an ambitious, multi-year research programme on equality in the workplace, Turing Fellow at the Alan Turing Institute, and Theme Lead for Behavioural and Experimental Data Science at the Institute for Data Science and Artificial Intelligence at Exeter. He conducts research on inequality and cooperation in three main domains: organisations, society and the environment. His research has been published in leading academic journals such as Nature, PNAS, Nature Human Behaviour, Leadership Quarterly, and Journal of Economic Behavior and Organization. To find out more about Oliver and his research, visit www.oliverhauser.org.

Prof Leif Isaksen

Leif Isaksen is Director of Digital Humanities at the University of Exeter and affiliated with the Department of Classics and Ancient History. He is also the Theme Lead for Digital Humanities, Creative Industries and Heritage Innovation at the Institute of Data Science and AI (IDSAI). His main interests are in spatial and temporal representation in the humanities - both in the ancient world and the modern one - and the paradigm of Linked Open Data to relate online resources about the past. This is most notably as director of several projects associated with the Pelagios Network, including the development of the Recogito annotation platform.

Professor Edward Keedwell

Ed Keedwell is Professor of Artificial Intelligence and a Turing Fellow. His research interests focus on the development of novel metaheuristics and hyper-heuristics for solving real-world problems in biology and engineering. His recent work explores the use of machine learning in optimisation and includes the development of human-in-the-loop optimisation techniques for engineering design problems and the use of sequences in online and offline hyper-heuristics in operational research. He has worked in this field for 20 years, publishing over 160 papers and receiving research funding of over £3M from EPSRC, Innovate UK, EU and industry.

Prof Mark Kelson

I am an Associate Professor of Statistics for Health and assistant director of the IDSAI. I am interested in helping people. Mostly their health. I use the tools I have been given. Mostly programming skill, numeracy and perseverance. I think science is the solution to our problems. Mostly. To aid search engine optimisation here's a random list of things I'm interested in: clinical trials, causal inference, open science, data science reproducibility, physical activity, mental health, coffee.

Dr Prakash Kripakaran

My research interests are broadly aimed at facilitating sustainable and resilient civil infrastructures, with a particular emphasis on the maintenance and management of bridge structures. My vision is to enable effective management of civil structural systems by using AI/data analytics to fuse weather forecasting and environmental data with bridge structural response and loading data, and derive meaningful, actionable information for use by infrastructure operators. I have an academic background in computer science, and a profound interest and track record in the application of computing techniques – including machine learning, system identification and signal processing techniques, for civil engineering problems such as bridge performance monitoring, structural design optimisation and flood resilience. Examples of recent projects include the modelling of thermal response of bridges using data-driven methods and computer vision-based technologies for damage detection. My research has been supported actively by stakeholders in the transportation sector including asset owners and operators, local authorities, consultants and policy makers.

Keywords: signal processing, machine learning, optimisation, data visualisation, human-computer interaction

Prof Francis Hugo Lambert

I am an natural scientist interested in the atmospheres and climates of the Earth and other planets -- particularly rocky planets orbiting other stars. My research uses data science techniques to carry out comparisons between model predictions and observations. My current work concerns the understanding and comparison of different approximate representations of small-scale processes known as "parameterisations" that must be used by numerical models due to computational constraints. Parameterisations are believed to be responsible for the majority of uncertainty seen in the prediction of future climate change on Earth, for example, but the group of parameterisations of a given process we have is arbitrary. The aim is to build and apply interpretable data science techniques that allow us to compare structurally different parameterisations of a given process to each other and observations or a physical model of the same process. it will then be possible to assess the contribution of different types of parameterisation to overall modelling uncertainty and determine whether the parameterisations we have properly represent our uncertainty in physical processes,

Dr Ke Li

I am currently a UKRI Future Leaders Fellow and a Senior Lecturer in Computer Science. My current research mainly at data-driven approaches for robust, personalized and interpretable multi-objective optimization and decision-making. I have also been enthusiastic at cross-disciplinary collaborations. Now I am working on interesting problems raised from water engineering, material discovery and bioscience.

Dr David Llwelyn

Prof David Llewellyn is based at the College of Medicine and Health at the University of Exeter where he co-leads the Mental Health Research Group. He has over 100 publications spanning dementia prevention, prediction, diagnostics, treatment, and care. He is Director of the Deep Dementia Phenotyping (DEMON) Network which applies data science and AI to dementia research and healthcare (www.demondementia.com). The DEMON Network has over 1,200 members from six continents and is funded by the Alan Turing Institute and Alzheimer’s Research UK. He sits on Alzheimer’s Research UK’s Grant Review Board and their Clinical Policy Advisory Panel. David is also the Exeter Institute for Data Science and Artificial Intelligence Health: Clinical Theme Lead, and a member of the Alan Turing Institute Health and Medical Sciences Programme Leadership Group.

Professor Ronaldo Menezes

Ronaldo Menezes is a Professor of Data and Network Science and head of the Computer Science department at the University of Exeter, and Director of the BioComplex Laboratory. His research interests include Network Science, Human Dynamics and Mobility, Complex Systems, and Urban Systems. He has spent 18 years in academia in the USA before moving to the University of Exeter in late 2018. His lab has received funding from the National Science Foundation (USA), Army Research Office (USA), CAPES (Brazil), FUNCAP (Brazil), to name a few. He has relevant skills in modelling human mobility and spatio-temporal data analysis, as evidenced by several publications including an extensive survey entitled “Human mobility: Models and applications”, a new model to capture human patterns called “The Effect of Recency to Human Mobility” and several other works in which human mobility modelling is used as a framework for solving real-world applications such as “social influence”, “sensor networks”, and “crime prediction”. He’s deeply involved with the research community in Human Mobility and Network Science as member of the steering committee of CompleNet and a board member of NetSci Society. He is also the co-editor-in-chief of one of the main journals in the field: Applied Network Science published by Springer Nature.

Prof Thomas Monks

Dr Thomas Monks is Associate Professor of Health Data Science at Exeter Medical School and IDSAI. His research interests are in modelling stochastic health care delivery with a focus on the real-time and data driven problems found in emergency care. His current research aims to combine computer simulation of health systems with reinforcement learning, and to advance open science within his discipline. Prior to joining University of Exeter he led an NIHR funded data science team at the University of Southampton that worked directly with the NHS

Prof Mohsen Mosleh

Mohsen is a Lecturer (Assistant Professor) at the University of Exeter Business School, a Fellow at Alan Turing Institute, and a Research Affiliate at MIT Sloan School of Management. Mohsen was a postdoctoral fellow at the MIT Sloan School of Management and the Department of Psychology at Yale University. Prior to his post-doctoral studies, Mohsen received his PhD from Stevens Institute of Technology in Systems Engineering with a minor in data science. He has five years of prior industry experience as a Software & Systems Integration Lead. Mohsen studies how misinformation and information biases on social media using digital field experiments, linking survey and digital data, and NLP. His work has been published in leading journals such as Nature, Nature Communications, PNAS, and CHI proceedings and has been covered in major media outlets including The Washington Post, the Telegraph and the Financial Times. 

Dr Jonathan Phillips

Jonathan Phillips is a UKRI Future Leaders Fellow and Turing Fellow who has pioneered novel experimental and statistical approaches in protein structural mass spectrometry and protein engineering. The Protein Choreography Group (PCG) study and engineer the structural dynamics of protein molecules: the mechanisms of biomolecular information processing. The PCG develop new statistical and mathematical models, software, experimental methods and instrumentation to determine the principles for structural control of protein function. Applied to natural systems, this yields insight into fundamental biology – ranging from allosteric control of kinases to Parkinson’s disease pathogenesis. Applied to drug molecules, this drives the design and creation of switchable proteins as novel biomedical tools.

Dr David Plans

David Plans initially studied artificial intelligence and media, using evolutionary algorithms to investigate the nature of human improvisation. He helped build the first European merger for Open Source startups and worked within the UK’s National Health Service to deploy the first mobile application to let users self-report in chronic illness. He has given papers and talks at the European Conference on Artificial Life, IRCAM, the Darwin Symposium, and the Computer Arts Society in London.

His first PhD focused on genetic algorithms for classification of human musical behaviour in MPEG7 time series. He then led BioBeats as CEO, a startup that focuses on machine learning models of mental health and disorder. Through his work at BioBeats, he has retrained as a neuroscientist and experimental psychologist, and is now pursuing DPhil work at the University of Oxford's Social Cognition Lab. He is a member of the INDEX group and a Senior Lecturer in Organisational Neuroscience."

Dr David Richards

Dr David Richards is an MRC Career Development Fellow based in the Living Systems Institute. After an undergraduate in physics, a PhD in string theory and a year tutoring at AIMS (a mathematics institute in Cape Town), he discipline-hopped to use mathematics and computing to investigate various areas of biology and medicine. His research involves mathematical modelling, computer simulation, image analysis, machine learning, data-centric analysis and parameter fitting. His past work has involved branching in the filamentous bacteria Streptomyces, telomere bouquet formation during meiosis, the accuracy of cell motion in chemotaxis, and the effect of ion channel noise in cells of the pituitary gland. He currently works on various aspects of phagocytosis (the way that immune cells engulf and destroy particles such as bacteria), the dynamics of peroxisomes (tiny organelles within cells), how filamentous fungi grow, and how plant cells respond to fungal attack. His MRC fellowship is based around using a joint experimental-modelling approach to study the role of both the cell and the target particle during phagocytosis including, for example, the dependence of phagocytic uptake on target shape and size. Website: www.exeter.ac.uk/davidrichards/

Dr Niccolò Tempini

Niccolò Tempini is Senior Lecturer in Data Studies at the University of Exeter, Department of Sociology, Philosophy and Anthropology, and a Turing Fellow at The Alan Turing Institute, where he serves as a member of the Ethics Advisory Group. He is an interdisciplinary social scientist interested in questions of information, data, technology, organisation, value and knowledge. He researches big data research and digital infrastructures, investigating the specific knowledge production economies, organisation forms and data management innovations that these projects engender with a focus in their social and epistemic consequences. He studies data practices looking for intersections between epistemological, organisational, socio-economic and ethical issues. His research has been published in international journals across science and technology studies, information systems, sociology and philosophy. More information at www.tempini.info.

Prof Krasimira Tsaneva-Atanasova

Krasimira Tsaneva-Atanasova earned her undergraduate and MSc degrees in mathematics at the University of Plovdiv, Bulgaria from 1991 until 1996. In September 2001 she started a PhD in applied mathematics at the University of Auckland, New Zealand. After completing her PhD in October 2004 she spent 18 months as a post-doctoral fellow at the Laboratory of Biological Modelling, National Institutes of Health, USA and another 15 months as a post-doctoral fellow at the Department of Mathematics and the Department of Biology at Ecole Normale Superieure in Paris, France. Krasimira joined the Department of Engineering Mathematics at the University of Bristol in October 2007 as a lecturer and was promoted to a Reader in Applied Mathematics in 2012. She moved to the College of Engineering, Mathematics and Physical Sciences, University of Exeter in July 2013 where she is currently a Professor of Mathematics for Healthcare.
She is Associate Dean for Global in the College of Engineering, Mathematics and Physical Sciences and Director of the EPSRC Hub for Quantitative Modelling in Healthcare (EP/T017856/1, 2021-2015). Her research and professional activities aim to inform novel applications of mathematics to enable the development of quantitative methods for healthcare and healthcare technologies.

Dr Jess Tyrell

I am a Senior Lecturer at the University of Exeter Medical School. My research interest focuses around using big data to improve our understanding of health and disease. I work in two main areas a) vestibular disorders and b) metabolic and mental health. My work in vestibular disorders has included developing a mobile phone application to monitor symptoms on a longitudinal basis and permit data linkage with environmental variables and has recently developed into an interdisciplinary collaboration to improve our understanding of balance control strategies in these individuals using a 6-dimensional moving platform known as the Vsimulator at the Exeter Science park. My work into the complex interplay between metabolic and mental health data involves using large population datasets, linked GP records and genetic data to test causal pathways. My team work at the forefront of genetic methodologies and are leading work into understanding how sociocultural factors are crucial in metabolic and mental health relationships.

Dr Kirsty Wan

Dr Kirsty Wan is a group leader at the Living Systems Institute. She obtained her undergraduate and PhD degrees in mathematics from DAMTP, University of Cambridge, where she also held a Nevile Junior Research Fellowship from Magdalene College for her postdoctoral work. She currently leads a highly interdisciplinary team researching micro-organismal behaviour, cell motility, and cognition. Projects in the group combine mathematical modelling, computer vision, trajectory data mining, with experimental biophysics, microscopy, and bio-inspired robotics. She is the recipient of major funding from the Academy of Medical Sciences and a Starting Grant from the European Research Council.

Prof Hywel Williams

Hywel is a computational scientist focused on problems that link social processes and environmental change. He is a faculty member in Computer Science, and affiliated to the Institute for Data Science & Artificial Intelligence and the Global Systems Institute, at University of Exeter. He is a Fellow of the Alan Turing Institute, the UK's premier facility for artificial intelligence and data science.

Prof Daniel Williamson

Daniel Williamson is Associate Professor of Bayesian Statistics at the University of Exeter. His areas of expertise include Uncertainty Quantification using Gaussian processes, Subjective Bayesian reasoning, decision support and Bayes linear theory.

Dr Andrew Wood

Andy wood is a lecturer in statistical genetics and health data science. His research focusses on the genetics of common disease and associated risk factors. He uses large population scale datasets to undertake his research. He is a recent recipient of an Academy of Medical Sciences Springboard award to investigate the genetics and impact of weight fluctuation over time on disease risk. He has been awarded a Turing Fellowship to focus on the challenges of using electronic health records from primary care data to detect patterns of weight change across 500,000 individuals living in the UK.

Dr Zena Wood

Dr. Zena Wood joined the Initiative for the Digital Economy (Index), University of Exeter Business School, in April 2019 as a Senior Research Fellow in the Digital Economy. Prior to this she was a Senior Lecturer in Spatial Informatics at the University of Greenwich. Her background is in Computer Science with her PhD (2011) focusing on the detection and identification of collective phenomena within movement data.

Her research focuses on how techniques from applied ontology and spatiotemporal reasoning can be used to derive value from datasets that would help understand the impact of digital transformation within the Digital Economy. She is particularly interested in the overlap between methods that can be applied to datasets related to physical and non-physical environments.

Prof Gavin Shaddick

Professor Shaddick is Chair of Data Science and Statistics at the University of Exeter and co-Director of the Joint Centre for Excellence in Environmental Intelligence, a joint research centre with the UK Met Office. He is Director of the UKRI funded Centre for Doctoral Training in Environmental Intelligence: Data Science and AI for Sustainable Futures and leads the Environment Sustainability Theme at the Alan Turing Institute, where he is a Turing Fellow.

Professor Shaddick’s research lies at the interface of AI, big data and environmental science. His research has led to major impact across a variety of fields including environmental modelling, epidemiology, global burden of disease and public health. He is an internationally recognised expert in Bayesian spatio-temporal modelling, and his methodological work is driven by the need for models and approaches which allow more accurate representation of complex systems in environmental modelling, policy support and health. He is the author of over 180 publications and co-author of two books: ‘The Oxford Handbook of Epidemiology for Clinicians’ and ‘Spatio-Temporal Modelling in Environmental Epidemiology’. He is a member of the UK government’s Committee on the Medical Effects of Air Pollutants (COMEAP) and the sub-group on Quantification of Air Pollution Risk (QUARK). He leads the World Health Organization’s Data Integration Taskforce for Global Air Quality and led the development of the Data Integration Model for Air Quality (DIMAQ) that is used to calculate of a number of air pollution related United Nations Sustainable Development Goals indicators.

Prof Tim Lenton

Tim Lenton is founding Director of the Global Systems Institute and Chair in Climate Change and Earth System Science at the University of Exeter. He has >25 years research experience, focused on modelling life’s coupling to the Earth system, biogeochemical cycling, climate dynamics, and associated tipping points. His books ‘Revolutions that made the Earth’ (with Andrew Watson) and ‘Earth System Science: A Very Short Introduction’ have popularised a new scientific view of our planetary home. Tim co-authored the ‘Planetary Boundaries’ framework and is renowned for his work identifying climate tipping points, which won the Times Higher Education Award for Research Project of the Year 2008. He has also received a Philip Leverhulme Prize 2004, European Geosciences Union Outstanding Young Scientist Award 2006, Geological Society of London William Smith Fund 2008, and Royal Society Wolfson Research Merit Award 2013. Tim is a member of the Earth Commission, an ISI Highly Cited Researcher, and in the top 100 of the Reuters ‘Hot List’ of the world’s top climate scientists.

Prof Sabina Leonelli

Sabina Leonelli is a Professor in Philosophy and History of Science at the University of Exeter, where she co-directs the Centre for the Study of the Life Sciences (Egenis). In 2021-22, she is also a Fellow of the Wissenschaftskolleg zu Berlin. Her research focuses on the methods and assumptions involved in the use of big data for discovery; the challenges involved in the extraction of knowledge from digital infrastructures, and the implications of choices in data curation for the outputs and uses of science and technology; the role of the open science movement within current landscapes of knowledge production, including concerns around inequality; and the status and history of experimental organisms as scientific models and data sources. She has received funding from several organisations, including twice from the European Research Council, and regularly acts as science policy advisor (including, this year, as part of the “Thinker programme” on reproducibility for the Belgian government). Her latest book is Data in Society: A Critical Introduction (with Anne Beaulieu), published in November 2021 by SAGE.

Prof Tim Dodwell

Professor Tim Dodwell leads the Data Centric Engineering Group at Exeter and is the interim Programme Director for the Data Centric Engineering Programme at the Alan Turing Institute. He holds a personal chair in Applied Mathematics which straddles the Department of Mechanical Engineering and the Institute of Data Science and AI at the University of Exeter. He currently holds a prestigious 5 year Turing AI Fellowship with the Alan Turing Institute and is the Romberg Visiting Professorship at Heidelberg in Scientific Computing. Externally he is on the editorial board for Proceedings of Royal Society London A, SIAM Journal of Uncertainty Quantification and Computer Physics Communications.

The Data Centric Engineering Group does leading research at the dynamic interface between applied mathematics, Bayesian statistics, machine learning and high-performance scientific computing. Spanning a broad range of research from fundamental theory in data science and AI to applied industrial focused projects. Prof Dodwell's group has rapidly expanded to >30 researchers and software engineers funded from UKRI, EPSRC, Innovate UK and direct industrial awards with a combined value exceeding £20.0M.

Prof Richard Everson

Richard Everson graduated with a degree in Physics from Cambridge University and a PhD in Applied Mathematics from Leeds University in 1988. He worked at Brown and Yale Universities on fluid mechanics and data analysis problems until moving to Rockefeller University, New York to work on optical imaging and modelling of the visual cortex. After working at Imperial College, London, he was appointed lecturer at Exeter University in 1999, where he is now a Professor of Machine Learning and Director of the Institute for Data Science and AI.