Interdisciplinary Research

Turing Fellows

A list of Edinburgh Turing Fellows

  Member Bio and Research
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Dr Gian Marco Campagnolo

Gian Marco is a Lecturer in Science, Technology & Innovation Studies at the University of Edinburgh. His research highlights aspects of business knowledge as apparent in client-consultant relationships as well as vendor-user interaction or in special conditions such as IT symposia and software demonstrations. When business turned heavily to analytics, he became interested in data science applications with a view on how ‘big data’ is made visible and turned into action.

In latest projects, Gian Marco's research addressed how empirical sociology can contribute to data-intensive research in understanding the evolution of careers in the IT sector. Taken up by the media and presented at the Alan Turing Institute inaugural workshop on Social Data Science, this research led to knowledge exchange with professional networking company LinkedIn.


Francesco Tudisco


Francesco Tudisco studied Computer Science and Mathematics at the University of Rome (Italy) and obtained a PhD in Computational Mathematics from the same University in December 2015. He then moved as a postdoc to Saarbruecken (Germany) and as a Marie Curie Individual Fellow at the University of Strathclyde soon afterwards. Before joining the University of Edinburgh as Reader in Machine Learning, he was Associate Professor of Numerical Analysis at the Gran Sasso Science Institute graduate school in L'Aquila (Italy).

Francesco’s research interests lie at the intersection between spectral theory, numerical analysis, network science and machine learning. His recent work includes the use of model-order reduction techniques from matrix and tensor differential equations to design efficient training algorithms for deep learning models, the numerical stability of deep neural networks and neural PDEs, the analysis of neural networks in the infinite width and infinite depth limits, nonlinear spectral theory with applications to machine learning on graphs, and the use of physics-informed and physics-inspired deep learning models in scientific simulations.


Giulia De Togni


Giulia De Togni is an interdisciplinary social scientist specializing in Science and Technology Studies, holding degrees in Social Anthropology (PhD, MSc), Japanese Studies (MSt, MPhil), and Legal Studies (BA). Recently awarded a Chancellor’s Fellowship at CMVM/Usher, she started this new position in January 2024, after having worked at Usher since 2019 on the Wellcome Trust-funded project “AI and Health,” and since 2022 as Principal Investigator on her Wellcome Trust Fellowship “Caring Machines.” For her Chancellor’s Fellowship, Giulia will continue her work on Responsible AI and Robotics Innovation in Healthcare which aims to enable different stakeholders to become part of the co-production of healthcare technologies to inform and shape innovation together.

Giulia is a Visiting Researcher at Edinburgh Centre for Robotics/Bayes; a Postdoctoral Affiliate of EFI; and she closely collaborates with teams of roboticists at the School of Engineering, Heriot-Watt University, National Robotarium, Alan Turing Institute, and Bristol Robotics Laboratory. Internationally, Giulia is PI of a project funded by JST (Japan’s Science and Technology Agency); closely collaborates with AIST (Japan’s National Institute of Advanced Industrial Science and Technology) and RIKEN (the largest research institute in Japan, with 3,000+ members). She is also a member of the INNOVCARE group (École des Hautes Études en Sciences Sociales); closely collaborates with roboticists based at KAIST (Korea Advanced Institute of Science and Technology); and is a Postdoctoral Affiliate of the IIT (Italian Institute of Technology).

Sara Wade

Sara Wade


Sara Wade is a Reader in Statistics and Data Science in the School of Mathematics, University of Edinburgh. Her research interests include statistics, machine learning, Bayesian analysis, with a focus on flexible methodology and efficient inference for complex data. She was co-Director of the Centre for Statistics (2019-2022), on the board of the International Society of Bayesian Analysis (ISBA) 2022-2025 and has served as Area Chair for Women in Machine Learning (WiML) workshops. Her work has been recognized by prizes including Best Doctoral Thesis by the Italian Statistical Society, Lindley Prize by ISBA, and Young Biometrician Award by the International Biometric Society.

Before joining the School of Mathematics, she was a Harrison Early Career Assistant Professor at the Department of Statistics, University of Warwick (2015-2018). She was a postdoctoral researcher in Machine Learning at the Computational and Biological Learning Laboratory, University of Cambridge working with Prof. Zoubin Ghahramani. She earned her PhD in Statistics in 2013 from Bocconi University, under the supervision of Prof. Sonia Petrone and Prof. Stephen Walker

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James Cheney James Cheney is a Reader in the Laboratory for Foundations of Computer Science, University of Edinburgh. He earned his PhD at Cornell University in 2004, moved to Edinburgh as a postdoctoral researcher and in 2008 was awarded a Royal Society University Research Fellowship. He currently holds an ERC Consolidator Grant and his research has also been funded by Microsoft Research, Google, LogicBlox, the US Air Force Office of Scientific Research, DARPA, EPSRC and the National Cyber Security Centre Research Institute on Verified Trustworthy Software Systems.
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Luciana Dadderio

Luciana D’Adderio is a Chancellor’s Fellow in Data Driven Innovation at the Usher Institute (University of Edinburgh) and a Turing Fellow with the Alan Turing Institute for Data Science and AI. Originally from a Science and Technology background, she holds a PhD in Science and Technology Policy and MSc in Technology & Innovation Management (both from SPRU, Sussex University).

Luciana’s research investigates the introduction of Artificial Intelligence in healthcare, currently focusing on AI-driven Innovation and Governance. Her research programme encompasses several key topics including: mapping and evaluating the AI innovation/adoption cycle (AI procurement, software validation, clinical evaluation, patient pathway definition and clinical workflow integration); designing and assessing frameworks for the continuous governance of AI, including algorithm testing, validation and audit; novel approaches for addressing AI ethics principles in practice (trustworthiness, explainability, dependability and risk); and mechanisms for supporting the development of an AI entrepreneurial ecosystem (including the creation of new AI- or data-driven healthcare innovations).

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Chris Dent

Chris Dent is Professor of Industrial Mathematics in the School of Mathematics at the University of Edinburgh, and held previous research and academic positions at Heriot-Watt, Marburg, Edinburgh and Durham Universities. He holds an MA in Mathematics (Cambridge University), PhD in Theoretical Physics (Loughborough University), and MSc in Operational Research (Edinburgh University). Since 2007 he has worked in energy systems analysis, which has expanded in recent years to a wider interest in decision support in areas of energy, infrastructure and public policy. He is a Fellow of the IET and of the Operational Research Society, a Chartered Engineer, and a Senior Member of the IEEE.
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Desmond Higham

Des Higham is Professor of Numerical Analysis in the School of Mathematics at the University of Edinburgh, where he is a member of the Applied and Computational Mathematics research group.

 He is also a Fellow of the Royal Society of Edinburgh and a Fellow of the Society for Industrial and Applied Mathematics. Des has a background in numerical analysis: the design, evaluation and implementation of computational methods for problems in applied mathematics. He has interests in network science, stochastic computation and machine learning, and their applications in human behaviour, online social media, epidemiology and life sciences.

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Oisin Mac Aodha

Oisin Mac Aodha is a Lecturer in Machine Learning in the School of Informatics at the University of Edinburgh. His current research interests are in the areas of computer vision and machine learning, with a specific emphasis on learning from limited supervision, extracting shape and structure from visual data, human-in-the-loop methods such as active learning and computer-assisted teaching, and applications in conservation technology.
Before coming to Edinburgh in 2019, he was a postdoctoral researcher at the California Institute of Technology (Caltech). He received his PhD in Computer Science in 2014 from University College London (UCL), an MSc in Machine Learning in 2008 also from UCL, and a BEng in Electronic Engineering from the National University of Ireland, Galway in 2007. 
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Mahesh Marina

I’m a Professor in the School of Informatics at the University of Edinburgh and a Turing Fellow at the Alan Turing Institute. At Edinburgh, I currently also serve as the Director of Institute for Computing Systems Architecture (one of the six research institutes within Informatics). Before joining Edinburgh, I had a two-year postdoctoral stint at the UCLA Computer Science Department in the Mobile Systems Laboratory headed by Prof. Rajive Bagrodia. I earned my PhD in Computer Science in 2004 from the State University of New York at Stony Brook, where my advisor was Prof. Samir Das. I have previously held visiting researcher positions at ETH Zurich and at Ofcom London. I’m a Distinguished Member of the ACM and a Senior Member of the IEEE.
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Dr Encarni Medina-Lopez Dr Encarni Medina-Lopez is a Chancellor’s Fellow in Data Driven Innovation, Space and Satellite at the School of Engineering. Her research interests pivot on the use of remote sensing to study the water-energy-environment trilemma, covering coastal and ocean engineering, and marine renewable energy applications. Her most recent work focuses on the use of machine learning for the prediction of sea surface salinity and temperature from multi-spectral open source satellite data. She leads the CEReS Research Group (Coastal & Environmental Remote Sensing Research Group), which studies topics related to fresh water quality indicators, lake and coastal dynamics, marine renewable energy, or sea chlorophyll estimation, all using satellite imagery and machine learning techniques.
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Liza Mijovic Liza Mijovic is a Senior Research Associate at the School of Physics and Astronomy. She works at the ATLAS experiment at the Large Hadron Collider (LHC), CERN. ATLAS is one of the LHC detectors. It is an international collaboration of about 3000 scientists which use data taken by ATLAS to perform research in high energy particle physics. Liza investigates the Higgs mechanism, which explains how elementary particles obtain masses. Due to the huge amount and complexity of the ATLAS data, machine learning plays an increasingly important role in her research. Liza is currently leading the Higgs data analysis at Edinburgh, and convenes the ATLAS group responsible for developing detector simulation for the future high-luminosity LHC. At Turing she is particularly interested in uncertainty-aware machine learning and synergy of her experimental particle physics work with other areas of research, such as medical imaging.
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Diego Oyarzún

Diego is a Turing Fellow and Reader in Computational Biology at the University of Edinburgh, with a joint post between the School of Informatics and the School of Biological Sciences. He works on computational methods for the study of molecular systems in biomedicine and synthetic biology. Some of his current projects include the use of data science methods for cell factory design, drug discovery, and various modalities of biomedical data.
At Edinburgh, Diego holds various leadership roles, including Deputy Director of the UKRI Centre for Doctoral Training in Biomedical AI, co-lead of the Science for Sustainability Hub, and Programme Director of the Cross-Disciplinary Fellowships at the Institute for Genetics and Cancer. He also had advisory roles with the World Economic Forum and the EPSRC Early Career Forum in Mathematical Sciences. In 2015 he was awarded a Young Investigator Grant from the Human Frontier Science Program and in 2017 he was selected as one of the 100 Young Global Changers, mandated by the G20 presidency. More information on his lab website and his University profile.
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Charles Raab Charles Raab (Politics and International Relations) is Professorial Fellow, having held the Chair of Government from 1999 to 2007 and from 2012 to 2015. He has served as a member of the academic staff since 1964, and has held visiting positions in the Oxford Internet Institute, the Tilburg Institute for Law, Technology, and Society (Tilburg University, The Netherlands), Queen’s University, Kingston, Ontario, and the Victoria University of Wellington (NZ). He was a Fellow at the Hanse-Wissenschaftskolleg (Institute for Advanced Study) in Delmenhorst, Germany, and is currently a Turing Fellow at the UK's Alan Turing Institute. With colleagues at the Universities of Stirling, St. Andrews and Essex, he is a Director of CRISP (Centre for Research into Information, Surveillance and Privacy (, and, and is a founder of the Scottish Privacy Forum. He is a Fellow of the Academy of Social Sciences (FAcSS) and a Fellow of the Royal Society of Arts (FRSA).
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Subramanian Ramamoorthy

Dr Subramanian Ramamoorthy is Professor and Chair of Robot Learning and Autonomy within the School of Informatics at the University of Edinburgh, where he has been on the faculty since 2007. He is an Executive Committee Member for the Edinburgh Centre for Robotics and at the Bayes Centre. He received his PhD in Electrical and Computer Engineering from The University of Texas at Austin in 2007. He is an elected Member of the Young Academy of Scotland at the Royal Society of Edinburgh, and has been a Visiting Professor at Stanford University and the University of Rome 'La Sapienza'.

He serves as Vice President - Prediction and Planning at FiveAI, a UK-based startup company focussed on developing a technology stack for autonomous vehicles. His research focus is on robot learning and decision-making under uncertainty, with particular focus on achieving safety and robustness in artificially intelligent systems.

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Michael Rovatsos Michael is Professor of Artificial Intelligence at the School of Informatics, Deputy Vice Principal of Research, and Director of the Bayes Centre at the University of Edinburgh. His academic research interests are mainly in multiagent systems, i.e. systems where either artificial or human agents collaborate or compete with each other. In recent years, much of his work has focused on ethical aspects of AI, primarily through the development of methods for designing fair and diversity-aware AI algorithms and architectures that adapt to the values of human users. Michael has authored around 100 scientific articles on various topics in AI, and has been involved in research projects that have received around £17 million of external funding.
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Sotirios Sabanis

Sotirios Sabanis is a Reader (Associate Professor) at the School of Mathematics of the University of Edinburgh. He received his undergraduate training in Mathematics at the Aristotle University of Thessaloniki and was awarded a PhD from Strathclyde University (Department of Statistics and Modelling Science), Glasgow. As Programme Director, he has led the development of the suite of postgraduate programmes in Computational Mathematical Finance at the University of Edinburgh and collaborates with industry (mainly financial services) through a number of joint projects. His research has appeared in the Annals of Applied Probability, SIAM Journal of Numerical Analysis, Stochastic Partial Differential Equations and other leading journals.
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André Santos André Santos is Lecturer in Predictive Analytics at the University of Edinburgh Business School. His research interests are mainly in quantitative finance, with an emphasis in multivariate volatility models, portfolio selection and optimization, and risk management, as well as in applications of machine learning methods to empirical asset pricing. His research has appeared in Quantitative Finance, Journal of Financial Econometrics, Journal of Economic Behaviour & Organization, Journal of Banking and Finance, Journal of Empirical Finance, among others. Prior to joining the University of Edinburgh, he was senior research fellow at the UC3M-Santander Big Data Institute where he collaborated in both research and consulting projects with industry and academic partners in machine learning and artificial intelligence. He holds a PhD in Quantitative Methods from Universidad Carlos III de Madrid.
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Ian Simpson

Ian Simpson is a Reader in Biological Informatics and Director of the UKRI Centre for Doctoral Training in Biomedical Artificial Intelligence in the School of Informatics at the University of Edinburgh. He holds a B.A. and an M.A. in Biochemistry (Oxford University) and a D.Phil. in Genetics (Oxford University) where he studied the epigenetic regulation of gene expression. He has spent over 20 years studying brain development and function at the molecular level and has developed novel methodologies and tools to analyse developmental gene expression data using graphical, dynamic, and evolutionary models.

His main research interests lie at the boundary between Informatics and Biomedicine and focus on jointly modelling molecular and clinical data in neurological disease to better understand the underlying molecular mechanisms and how they relate to emergent phenotypes in patients. This approach has the potential to revolutionise diagnosis, prognosis, and treatment improving outcomes for patients and helping clinicians to benefit from the promise of data driven analytics in healthcare. Ian is a strong advocate of open science and public engagement in research, is a member of the Open Bioinformatics Foundation (OBF), an alumnus of the Royal Society of Edinburgh Young Academy of Scotland, and an active STEM Ambassador.

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Dr Lukasz Szpruch

Lukasz is a Lecturer at the School of Mathematics, University of Edinburgh. Before moving to Scotland he was a Nomura Junior Research Fellow at the Institute of Mathematics, University of Oxford, and a member of Oxford-Man Institute for Quantitative Finance. He holds a Ph.D. in mathematics from University of Strathclyde in Glasgow.

Lukasz’ research interests are in theoretical and applied probability theory. He has a particular interest in:

  • (Multilevel) Monte Carlo Methods
  • Stochastic Interacting Particle Systems and Mean Filed Limits; Stochastic McKean-Vlasov Equations
  • Sequential Monte Carlo (Particle Filters)
  • (Stochastic) Gradient type algorithms and their applications in statistics and optimization
  • Highly dimensional stochastic systems such as SDEs, BSDEs and SPDE
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Melissa Terras

Melissa Terras is the Professor of Digital Cultural Heritage at the University of Edinburgh‘s College of Arts, Humanities, and Social Sciences, which she joined in October 2017, leading digital aspects of research within CAHSS at Edinburgh, as well as building digital capacity in the new Edinburgh Futures Institute. Her research focuses on the use of computational techniques to enable research in the arts, humanities, and wider cultural heritage and information environment that would otherwise be impossible. She is a Turing Institute Fellow 2018-2020. 

With a background in Classical Art History and English Literature (MA, University of Glasgow), and Computing Science (MSc IT with distinction in Software and Systems, University of Glasgow), her doctorate (Engineering, University of Oxford) examined how to use advanced information engineering technologies to interpret and read Roman texts. Employed at UCL Department of Information Studies from 2003, she was made Honorary Professor of Digital Humanities upon her departure from UCL in 2017, and Honorary Professor in UCL Centre for Digital Humanities, which she directed 2012-2017. Terras was previously Vice Dean of Research in UCL’s Faculty of Arts and Humanities (2014-2017).

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Ivan Titov is a Reader at the University of Edinburgh and an Associate Professor at the University of Amsterdam. His research focuses on natural language understanding (including semantic parsing, question answering and information extraction), natural language generation (e.g., machine translation) and generally on machine learning for natural language processing (NLP).  His current research aims at making deep learning-based NLP models robust,  interpretable, and controlable by human experts. Ivan's research is supported by an ERC Starting Grant and a Dutch VIDI Fellowship, as well as contributions from industry (e.g., Google, SAP,  Yandex, and  He is a co-director of Edinburgh CDT in NLP and the ELLIS NLP Fellow Program, and served as a program co-chair for ICLR 2021 and CoNLL 2018.  See for further information.
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Sotirios Tsaftaris

 Prof. Sotirios A. Tsaftaris, or Sotos, (;; @STsaftaris), is currently the Canon Medical/Royal Academy of Engineering Research Chair in Healthcare AI, and Chair (Full Professor) in Machine Learning and Computer Vision at the University of Edinburgh (UK). He is also a Turing Fellow with the Alan Turing Institute. Previously he held faculty positions with IMT Institute for Advanced Studies Lucca (Italy) and Northwestern University (USA).

He has published extensively, particularly in interdisciplinary fields, with more than 140 journal and conference papers in his active record. His research interests are machine learning, computer vision, image analysis and processing. 

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Thanasis Tsanas

Thanasis is a Senior Lecturer (Associate Professor) in Data Science at the Usher InstituteEdinburgh Medical SchoolUniversity of Edinburgh. He studied Engineering and completed a DPhil (PhD) in Applied Mathematics at the University of Oxford (2012). He worked at the University of Oxford as a Research Fellow in Biomedical Engineering and Applied Mathematics (2012-2016), Stipendiary Lecturer in Engineering Science (2014-2016), and Lecturer in Statistical Research Methods (2016-2019).

He is Co-founder of the NHS Digital Academy, the first national NHS digital health leadership programme, where he leads the development and delivery of 'Clinical Decision Support and Actionable Data Analytics'. He leads the interdisciplinary Data Analytics Research and Technology in Healthcare (DARTH) group, and is Co-Director of Telescot. He received the Andrew Goudie award (top PhD student across all disciplines, St. Cross College, University of Oxford, 2011), the EPSRC Doctoral Prize award (2012), the young scientist award (MAVEBA, 2013), the EPSRC Statistics and Machine Learning award (2015), and the BIOSTEC/Biosignals best paper award (2021). He won a ‘Best reviewer’ award from the IEEE Journal of Biomedical Health Informatics and an ‘Outstanding reviewer’ award from the journal Computers in Biology and Medicine. His research work has been commercially explored by a number of industrial partners including Intel Corporation, and has been highlighted in the media e.g. Reuters.

Thanasis sits on the Editorial Boards of JMIR Mental Health, JMIR mHealth and uHealth, and Frontiers in Neurology. He is a Senior Member of IEEE, a Fellow of the Higher Education Academy, and a Fellow of the Royal Society of Medicine.

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Dr Lachlan Urquhart I am a Lecturer in Technology Law at the University of Edinburgh, Visiting Researcher and Founding Member of Horizon Digital Economy Research Institute, and a Turing Fellow at the Alan Turing Institute. I have a multidisciplinary background in computer science (PhD) and law (LL.B; LL.M). My main research interests are in human computer interaction, ubiquitous computing, data protection and cybersecurity. I have been Co-I on projects totalling over £5m from EPSRC, ESRC, AHRC, Universitas 21, Impact Accelerator Funds, and Research Priority Funds. I have published widely in leading venues in HCI (e.g. TOCHI, ACM Ubicomp, DIS, Personal & Ubiquitous Computing), law (e.g. International Data Privacy Law, International Journal of Law & Technology, International Review of Law, Computers & Technology) and ethics (e.g. Journal of Responsible Innovation, Philosophy & Technology, ACM Computers & Society). My work has also been featured in New Scientist, Wired and The Register. For recent publications and project activities see here.
Shannon Vallor
Shannon Vallor Shannon Vallor is the Baillie Gifford Chair in the Ethics of Data and Artificial Intelligence and Director of the Centre for Technomoral Futures in the Edinburgh Futures Institute at the University of Edinburgh, where she is also appointed in the Department of Philosophy. Professor Vallor's research explores how emerging technologies reshape human moral and intellectual character, and maps the ethical challenges and opportunities posed by new uses of data and artificial intelligence. Her work includes advising academia, government and industry on the ethical design and use of AI, and she is a former Visiting Researcher and AI Ethicist at Google. Her current project examines responsibility gaps in the governance of autonomous systems, as part of the EPSRC-funded Governance node of the UKRI Trustworthy Autonomous Systems programme. She is the author of Technology and the Virtues: A Philosophical Guide to a Future Worth Wanting (Oxford University Press, 2016) and editor of the forthcoming Oxford Handbook of Philosophy of Technology.
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Catalina Vallejos

Catalina is a Chancellor's Fellow at the MRC Human Genetics Unit, where she leads the Biomedical Data Science research group

Before moving to Edinburgh, Catalina was part of the first cohort of Turing Research Fellows. As part of her Fellowship, Catalina was also a Group Leader within the Lloyds Register Foundation-Turing Programme on Data-Centric Engineering

Between 2014 and 2016, Catalina was a Postdoctoral Fellow in a joint appointment between the MRC Biostatistics Unit (MRC-BSU) and the EMBL European Bioinformatics Institute (EMBL-EBI), both located in Cambridge (UK). In this position, she was a member of the Statistical Genomics research group (MRC-BSU) and the Marioni group (EMBL-EBI) which are respectively lead by Professor Sylvia Richardson and Dr John Marioni

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John Vines John Vines is Chair in Design Informatics, and co-directs the Institute of Design Informatics at the University of Edinburgh. His research is in the interdisciplinary field of human-computer interaction (HCI). He specialise in the use of design-led approaches and methodologies – often referred to as ‘research through design’ (RtD) – to understand, explore, prototype, and evaluate interactive and data-driven systems. His research engages with various contexts where data-driven technologies are increasingly applied - this includes ageing and later life, community health and wellbeing, financial services, and civic participation and public services. He has been an investigator on research grants totalling over £25m. This has included two large EPSRC digital economy research centres (the Digital Economy Research Centre (2014-2021) and Centre for Digital Citizens (2020-2025), and a range of projects funded through the UKRI Digital Economy theme (such as the OxChain project exploring distributed ledger technologies in the charity sector, and Playing Out with IoT project exploring the potential of the internet of things to promote outdoor play for young children).
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Amy Wilson

I am a lecturer in industrial mathematics with a background in applied statistics for problems in industry and government. Applications I have worked on include the modelling of extremes for capacity adequacy studies (assessing the risk of shortfalls), emulation of large scale energy planning models, decision-making under uncertainty and statistics and the law.

I previously held postdoctoral positions at the Universities of Durham and Edinburgh in statistical modelling for energy systems. My PhD, at the University of Edinburgh, was in statistical modelling for the evaluation of forensic evidence. This focused on the evaluation of autocorrelated evidential data, in particular for evidence relating to traces of cocaine on banknotes.

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Professor Chris Williams

Chris Williams is Professor of Machine Learning in the School of Informatics, University of Edinburgh. He obtained his MSc (1990) and PhD (1994) at the University of Toronto, under the supervision of Geoff Hinton. He was a member of the Neural Computing Research Group at Aston University from 1994 to 1998, and has been at the University of Edinburgh since 1998.

He is interested in a wide range of theoretical and practical issues in machine learning, statistical pattern recognition, probabilistic graphical models and computer vision. This includes theoretical foundations, the development of new models and algorithms, and applications. His main areas of research are in models for understanding time-series, visual object recognition and image understanding, unsupervised learning, and Gaussian processes. At the Turing he also has interests in improving the data analytics process, looking to address the issues of data understanding and preparation that are widely quoted as taking around 80% of the time in a typical data mining project.

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Dr Maria Wolters

Maria's research focuses on supporting people with long-term conditions live rich and meaningful lives. She has a background in computational linguistics and speech science (PhD, 2000, University of Bonn), human-computer interaction, assistive technology, and eHealth, and a long-term interest in statistics.

Maria is interested in why people collect health data. In particular, missing data is often missing for a reason. This could be because the smartphone is out of battery, or because one has no energy left to complete a diary entry. Maria wants to find patterns of missing data and describe them qualitatively and quantitatively in a way that leads to new insight – in other words, treating missingness as information.

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Dr Kostas Zygalakis

Kostas received his PhD in computational stochastic differential equations from University of Warwick at 2009 and held postdoctoral positions at the Universities of Cambridge, Oxford and the Swiss Federal Institute of Technology, Lausanne. In 2011 he was awarded a Leslie Fox Prize (IMA UK) in numerical analysis. Before joining Edinburgh as a lecturer in the mathematics of Data Science in 2016, he held a lectureship in Applied Mathematics at the University of Southampton.

Kostas research is on the interplay between numerical analysis and computational statistics. In particular, he is interested in the properties of long time approximation to stochastic differential equations and their connection to sampling algorithms. Furthermore, he has recently started working on problems related to Data Assimilation and Bayesian inverse problems, as well as applications of ideas of numerical analysis to machine learning algorithms.