51cg

Professor Georgios Leontidis

Professor Georgios Leontidis
Professor Georgios Leontidis
Professor Georgios Leontidis

Professor, ELLIS Society Member

Personal Chair

51cg
Email Address
georgios.leontidis@abdn.ac.uk
School/Department
Provost





Biography

I am currently on sabbatical at UiT the Arctic University of Norway but I maintain my affiliation with UoA. I am a Chair in Machine Learning (at the point of promotion, aged 35, I was within the ~0.4% of the UK's youngest full professors) and a former member of the Scottish AI Alliance Leadership Member. I have a strong interest in both theoretical aspects of Machine/Deep Learning, e.g. capsule networks, domain adaptation, self-supervised learning etc., as well as applications, e.g. data imputation in environmental data of COSMOS-UK network (PI in NERC/EPSRC project ENTRAIN - NE/S016236/1, NE/S016244/1), homomorphic encryption with deep learning for enabling data sharing and analytics in food industry (PI - IoFT network plus EPSRC project), anomaly detection in nuclear reactors (Co-PI in H2020 project Cortex - 20 EU partners), Optimising retail refrigeration systems with machine learning (Co-PI-IUK project with Tesco), forecasting yield in strawberries and tomatoes (Co-PI-EU Interreg project SmartGreen and PhD studentship), Gas Turbine availability and fault prediction with Siemens Lincoln, etc. I am currently leading/co-leading several funded projects, including Enhancing Agri-Food Transparent Sustainability (PI), Predictive Emissions Monitoring System for Gas Turbines with Siemens Energy and Machine Learning and Expert-based System for Soft Fruit Yield Forecasting (Data Lab and Angus Soft Fruits).

Previously I was a Senior Lecturer at the University of Lincoln, a Senior Data Scientist at IBA Dosimetry in Germany and a Marie Curie ITN Fellow.

I serve as reviewer/AC in various top venues, such as NeurIPS/ICML/AAAI/ICLR, and participated in the UK AI Council’s Data Working Group ecosystem. I am also a member of the Full College of EPSRC and a panel college member of the UKRI FLF. I am also an External Examiner at Cranfield University (MSc applied AI).

 

Memberships and Affiliations

Internal Memberships
University Management Group (2022-March 2026)
University Research Committee (2024-March 2026)
Information Governance Committee (2023-March 2026)

Senate member (2020-2022)

Senate Business committee member (2020-2022)

External Memberships

BMVA 2025/2026 Summer School Chair

BMVC 2023 co-organiser and co-chair for ACs and Reviewers selection

Senior Expert Network, NERC Constructing a Digital Environment Programme ()

External Examiner of the MSc in Applied AI at Cranfield University (2020-2023)

External Examiner of the BSc in Computer Science, Hull University (2018-2022)

Sift and Interview panel member of the UKRI Future Leaders Fellowship scheme

Full College of EPSRC - member

AI Council’s Data Working Group ecosystem - member

Latest Publications

  • Enhancing Strawberry Yield Forecasting with Backcasted IoT Sensor Data and Machine Learning

    Ayall, T. A., Li, A., Beddows, M. A., Markovic, M., Leontidis, G.
    IEEE Transactions on AgriFood Electronics
    Contributions to Journals: Articles
  • Bottom-up emissions and energy assessment for decommissioning offshore platform structures in the North Sea: Brent and Tern case studies

    Jalili, S., Leontidis, G., Stone, M., Neilson, R.
    Environmental impact assessment review, vol. 119, 108398
    Contributions to Journals: Articles
  • VisionTreeS: A Hybrid Tree-Based Visual Masked Autoencoder Approach for Strawberry Yield Forecasting From Low-Resolution Data

    Beddows, M. A., Durrant, A., Leontidis, G.
    IEEE Transactions on AgriFood Electronics, vol. 4, no. 1, pp. 128-136
    Contributions to Journals: Articles
  • Personalised Multi-Task Federated Learning for Diabetic Retinopathy Medical Image Classification

    Li, Y., Sharma, P., Leontidis, G., Yi, D.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • Assessing the impact of nature-based solutions on soil health in sub-Saharan Africa through farmer-centred methods

    Bittner, D., Smith, J., Leontidis, G., Campbell, G. A., Biegel, J., Smith, P., Kuhnert, M., Skalský, R., Giuliani, L. M., Salik, A. W., Hallett, P., Burslem, D. F. R. P., Yakob, G., Mekuria, W., Phimister, E., Haileslassie, A., Tegegne, D., Norouzi, S., Chen, H., Gubry-Rangin, C., Khan, A.
    Environmental Research Letters, vol. 21, no. 4, 043004
    Contributions to Journals: Articles

View My Publications

Prizes and Awards

- TMLR - Action Editor

- ICLR 2025,2026 - Area Chair

- NeurIPS 2024,2025 - Area Chair

- Best Area Chair award, NLDL 2025

- Shortlisted, AUSA/UoA for "Outstanding Contribution to Accessibility and Inclusivity in Blended Learning (2021)"

- Ranked at Top 4% of the EPSRC Full Peer Review College

- NeurIPS 2020 & 2023, top 10% Reviewer out of ~7000

- EU commission FISA 2019 conference - best PhD paper award (PhD student:Aiden Durrant)

Research

Research Overview

I am interested in problems revolving around deep learning and machine learning, more specifically on domain adaptation, variational inference and self-supervised learning. I am also working on novel neural network architectures, such as Capsule Networks.

In terms of application areas, I have a strong interest in problems that ML can provide solutions primarily in environmental, industrial, food and healthcare settings.

My past and current activity involves working with national and international collaborators on nuclear reactor perturbation analysis, optimising retail refrigeration systems, gap filling in environmental time-series, domain adaptation for food retail packaging image quality detection, disease detection, and yield forecasting for strawberries and tomatoes

Research Specialisms

  • Artificial Intelligence
  • Neural Computing
  • Computer Vision
  • Machine Learning

Our research specialisms are based on the Higher Education Classification of Subjects (HECoS) which is , published under the licence.

Current Research

I am currently working on the following problems across several funded projects:

a) Agrifood sustainability with AI across several funded projects, such as the UKRI AI CDT SUSTAIN () and the EPSRC EATS project (

b) New routing algorithms for Capsule Networks in order to improve their run time and performance, whilst reducing the number of parameters

c) Yield forecasting for strawberries - we use mobile robots to collect data in a setting that our collaborators at the Univeristy of Lincoln have in the Riseholme campus. We collect time-series, image, depth and video data, so that we can develop new machine learning techniques that can accurately and robustly forecast yield in 1-, 2- and 3- weeks ahead

d) Predicting availability of gas turbines, a collaboration with Siemens Energy Industrial Turbomachinery Ltd.

Past Research

--Gap filling in environmental time-series, specifically for the Cosmos-UK network. We developed new data imputation techniques in order to fill the gaps in the time series using historical data from most of the Cosmos-UK sites across the UK () - project funded by NERC, EPSRC and Defra (NERC-led)

--Optimising demand side response of retail refrigeration systems with Machine Learning, a collaboration with Tesco and funded by Innovate UK

 

Collaborations

UK:

a) Centre for Ecology and Hydrology, Wallingford, with Matt Fry, Jon Evans, Steve Cole, Mike Bowes and John Wallbank

b) British Geological Survey, Keyworth, with Andy Kingdon and john Bloomfield

c) Sheffield University, Mike Mangan

d) University of Lincoln, MLearn group, LIAT and LCAS groups

e) Siemens Energy Industrial Turbomachinery Ltd.

International:

a) Chalmers University of Technology, Sweden with Christophe Demaziere and Paolo Vinai

b) Paul Scherrer Insitute, Switzerland with Hamid Dokhane

c) National Technical University of Athens, Greece with Andreas Stafylopatis and Georgios Alexandridis

d) Technical University of Madrid, Spain with Cristina Montalvo

e) Nuclear plant, UJV/Rez, Czech Republic, with Petr Stulik

Funding and Grants

    • Enhancing Agri-Food Transparent Sustainability (Uni of Aberdeen, with Universities of Nottingham and Dundee, and Scotland’s Rural College) – EPSRC£1.1M PI 01/2022 to 12/2024
    • UKRI AI Centre for Doctoral Training in Sustainable Understandable agri-food Systems Transformed by Artificial INtelligence (SUSTAIN) – UKRI/EPSRC£10.9M co-Director – 04/2024 to 09/2032, with Universities of Lincoln (lead), Queen’s Belfast and Strathclyde; >60 PhD studentships split equally across the four partners (£1.7M as local PI)
    • AI in the Biosciences Network (AIBIO-UK) – BBSRC£2MCo-I 09/2023 to 08/2028 led by the University of Nottingham, with Universities of Aberdeen, KCL, Manchester, Aberystwyth, Bristol, and Quadram Institute
    • A4IM: Affordable Low-field MRI Reference System – EURAMET-EU – £110K for Aberdeen (£3M in total) – Co-I – 09/2023 to 09/2026
    • More real than reality: using deep learning and generative models to resolve how people make sense of other people’s behaviour – SGSSS-ESRC PhD£70K – 10/2023 to 10/2027
    • Data for Net Zero (D4NZ) – Net Zero Technology Centre Ltd £1,06M – Co-I (work package leader on decision making) – 1/1/2022 to 01/05/2025
    • Machine Learning algorithms for microfluidic precision oncology assays – CENSIS – PI – £49,789 – 01/2023 to 01/2024
    • 20 Tenure-Track Interdisciplinary Fellows and 12 Interdisciplinary PhD studentships – Development Trust - £4M – Co-PI (jointly the five Interdisciplinary Directors, 51cg)
    • WYSA – NIHR consultancy on a protocol for AI application for mental health management – £50K (across 12 people) – 05/2022 to 05/2023
    • Machine Learning and Expert-based System for Soft Fruit Yield Forecasting – Data Lab Industrial Doctorate with Angus Soft Fruits £66K – 07/2021 to 07/2024
    • Opening the black box: helping AI to persuade without bias – SGSSS-ESRC PhD£70K – 10/2022 to 10/2026
    • Next Generation self-supervised Learning Systems for Vision Tasks – EPSRC HPC – PI44,640 GPU hours
    • iCASE EPSRC 4-year PhD studentship with Siemens Industrial Turbomachinery on Machine Learning for predictive emissions monitoring systems for gas turbine – PI - £118K (£89K EPSRC || £29K Siemens Energy)– 10/2021-10/2025
    • Data Trusts and Data Sharing in Food Supply Chains – EPSRC IoFT Network Plus £12K – PI – (£50K in total) – 09/2020-03/2021
    • NEXTGEN: Neural-network Encryption; eXploration of Techniques for secure aGricultural data processing – EPSRC IoFT Network Plus - £28K (£50K in total) PI – 03/2020 to 09/2020
    • Engineering Transformation for the Integration of Sensor Networks – NERC£114K PI – (£340K in total) – 02/2019 to 06/2020
    • Out of War Experiences: Hope for the Future (Metadata Analytics) – 165K£ (280K£ including match-funding) – EU H2020Co-I – University of Lincoln Coordinating – 2019 to 2022
    • BerryPredictor: Improving harvest forecasts, yield predictions and crop productivity by optimising zonal phytoclimates in covered strawberry production – Innovate UK – £80,000 Co-I – 09/2019 to 08/2022
    • CORe monitoring Techniques and Experimental validation and demonstration – EU H2020 £155K - Co-PI for Uni of Lincoln (~£5M in total) – 09/2017 to 08/2021 –
    • SmartGreen–Big Data and Eco-Innovative resource use in the NSR Greenhouse Industry – EU Interreg £530,000 (with 50% match funding) for UoL (~3M£ in total) – 09/2017 to 08/2021
    • The Development of Dynamic Energy Control Mechanisms for Food Retailing Refrigeration Systems – Innovate UK 845,510£ - Co-I for Uni of Lincoln (~3.5M in total) – 09/2016 to 11/2018 -
    • ReACT Refrigeration AI Control Technologies – BBSRC seeding catalyst £ 35K - Co-I – 10/2018 to 04/2019
    • Precision Agriculture: Machine Learning for Yield Prediction and Uncertainty Estimation - BBSRC-CTP-NPIF 4-year PhD studentship with NIAB - £99,300 - 10/2020 to 10/2024
Publications

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  • Graph Neural Networks for Reservoir Level Forecasting and Draught Identification

    Durrant, A., Haro Monteagudo, D., Leontidis, G.
    EGU General Assembly 2022
    Contributions to Conferences: Abstracts
  • The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector

    Durrant, A., Markovic, M., Matthews, D., May, D., Enright, J., Leontidis, G.
    Computers and Electronics in Agriculture, vol. 193, 106648
    Contributions to Journals: Articles
  • Fully Homomorphically Encrypted Deep Learning as a Service

    Onoufriou, G., Mayfield, P., Leontidis, G.
    Machine Learning and Knowledge Extraction, vol. 3, no. 4, pp. 819-834
    Contributions to Journals: Articles
  • Detection and Localisation of Multiple In-core Perturbations with Neutron Noise-based Self-Supervised Domain Adaptation

    Durrant, A., Leontidis, G., Kollias, S., Torres, A., Montalvo, C., Mylonakis, A., Demaziere, C., Vinai, P.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • Contrastive Domain Adaptation

    Thota, M., Leontidis, G.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • Commentary to the Commission’s proposal for the “AI Act” – Response to selected issues

    Cserne, P., Ducato, R., Zivkovic, P., Brown, A., Couzigou, I., Leontidis, G., Oren, N., Sutherland, C., Sweeney, P., Yuksel Ripley, B.
    Non-textual Forms: Web Publications and Websites
  • Deep Learning for Computer-Aided Diagnosis in Ophthalmology: A Review

    Brown, J., Leontidis, G.
    State of the Art in Neural Networks and Their Applications. El-Baz, S., Suri, J. S. (eds.). 1st edition. Elsevier, pp. 219-237, 19 pages
    Chapters in Books, Reports and Conference Proceedings: Chapters
  • An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth

    Alhnaity, B., Kollias, S., Leontidis, G., Jiang, S., Schamp, B., Pearson, S.
    Information Sciences, vol. 560, pp. 35-50
    Contributions to Journals: Articles
  • How might technology rise to the challenge of data sharing in agri-food?

    Durrant, A., Markovic, M., Matthews, D., May, D., Leontidis, G., Enright, J.
    Global Food Security, vol. 28, 100493
    Contributions to Journals: Articles
  • Lightweight deep learning models for detecting COVID-19 from chest X-ray images

    Karakanis, S., Leontidis, G.
    Computers in Biology and Medicine, vol. 130, 104181
    Contributions to Journals: Articles
  • Neutron Noise-based Anomaly Classification and Localization using Machine Learning

    Demaziere, C., Mylonakis, A., Vinai, P., Durrant, A., Ribeiro, F. D. S., Wingate, J., Leontidis, G., Kollias, S.
    Contributions to Conferences: Papers
  • Multi-source domain adaptation for quality control in retail food packaging

    Thota, M., Kollias, S., Swainson, M., Leontidis, G.
    Computers in Industry, vol. 123, 103293
    Contributions to Journals: Articles
  • Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

    Alhnaity, B., Pearson, S., Leontidis, G., Kollias, S.
    Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
  • Data Sharing and Interoperability for Data Trusts Workshop: Summary Report

    Markovic, M., Leontidis, G., Matthews, D., Enright, J., Durrant, A., May, D.
    Zenodo. 3 pages.
    Other Contributions: Other Contributions
  • Introducing Routing Uncertainty in Capsule Networks

    De Sousa Ribeiro, F., Leontidis, G., Kollias, S.
    Contributions to Conferences: Papers
  • Imputation of missing sub-hourly precipitation data in a large sensor network: a machine learning approach

    Chivers, B., Wallbank, J., Cole, S., Sebek, O., Stanley, S., Fry, M., Leontidis, G.
    Journal of Hydrology, vol. 588, 125126
    Contributions to Journals: Articles
  • Introducing Multi-Source Domain Adaptation for Quality Control in Retail Food Packaging

    Thota, M., Leontidis, G.
    Contributions to Conferences: Other Contributions
  • Results of the application and demonstration calculations

    Alexandridis, G., Blaesius, C., Demaziere, C., Destouches, C., Dokhane, A., Durrant, A., Fiser, V., Girardin, G., Herb, J., Ioannou, G., Jacqmin, R., Knospe, A., Kollias, S., Lange, C., Leontidis, G., Lipcsei, S., Montalvo, C., Mylonakis, A., Pantera, L., Perin, Y., Pohl, C., Seidl, M., Stafylopatis, A., Stulik, P., Torres, L. A., Verdu Martin, G., Verma, V., Vidal-Ferràndiz, A., Viebach, M., Vinai, P.
    European Commission. 194 pages.
    Other Contributions: Other Contributions
  • Deep Bayesian Self-Training

    Ribeiro, F. D. S., Calivá, F., Swainson, M., Gudmundsson, K., Leontidis, G., Kollias, S.
    Neural Computing and Applications, vol. 32, pp. 4275-4291
    Contributions to Journals: Articles
  • The Augmented Agronomist Pipeline and Time Series Forecasting

    Onoufriou, G., Hanheide, M., Leontidis, G.
    3rd UK Robotics & Autonomous Systems Conference (UK-RAS)
    Contributions to Conferences: Posters
  • An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth

    Alhnaity, B., Kollias, S., Leontidis, G., Jiang, S., Schamp, B., Pearson, S.
    Working Papers: Working Papers
  • Nemesyst: A hybrid parallelism deep learning-based framework applied for internet of things enabled food retailing refrigeration systems

    Onoufriou, G., Bickerton, R., Pearson, S., Leontidis, G.
    Computers in Industry, vol. 113, 103133
    Contributions to Journals: Articles
  • Capsule Routing via Variational Bayes

    Ribeiro, F. D. S., Leontidis, G., Kollias, S.
    Contributions to Conferences: Papers
  • Development of machine learning techniques and evaluation of analysis results

    Kollias, S., Stafylopatis, A., Leontidis, G., Alexandridis, G., Tabouratzis, T., Durrant, A.
    European Commission. 42 pages.
    Other Contributions: Other Contributions
  • 3D Convolutional and Recurrent Neural Networks for Reactor Perturbation Unfolding Anomaly Detection

    Durrant, A., Leontidis, G., Kollias, S.
    9th European Commission Conference on EURATOM Research and Training in Safety of Reactor Systems
    Contributions to Conferences: Posters
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