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
2026 - Accepted/In press
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
2026 - Published
Jalili, S., Leontidis, G., Stone, M., Neilson, R.
Environmental impact assessment review, vol. 119, 108398
Contributions to Journals: Articles
- [ONLINE] DOI:
- [OPEN ACCESS]
- [ONLINE]
VisionTreeS: A Hybrid Tree-Based Visual Masked Autoencoder Approach for Strawberry Yield Forecasting From Low-Resolution Data
2026 - Published
Beddows, M. A., Durrant, A., Leontidis, G.
IEEE Transactions on AgriFood Electronics, vol. 4, no. 1, pp. 128-136
Contributions to Journals: Articles
- [ONLINE] DOI:
- [OPEN ACCESS]
Personalised Multi-Task Federated Learning for Diabetic Retinopathy Medical Image Classification
2026 - E-pub ahead of print
Li, Y., Sharma, P., Leontidis, G., Yi, D.
Chapters in Books, Reports and Conference Proceedings: Conference Proceedings
- [ONLINE] DOI:
- [OPEN ACCESS]
Assessing the impact of nature-based solutions on soil health in sub-Saharan Africa through farmer-centred methods
2026 - Published
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
- [ONLINE] DOI:
- [OPEN ACCESS]

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 – £2M – Co-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 – PI – 44,640 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 H2020 – Co-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

Update

51cg

Biography

Memberships and Affiliations

Latest Publications

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

Bottom-up emissions and energy assessment for decommissioning offshore platform structures in the North Sea: Brent and Tern case studies

VisionTreeS: A Hybrid Tree-Based Visual Masked Autoencoder Approach for Strawberry Yield Forecasting From Low-Resolution Data

Personalised Multi-Task Federated Learning for Diabetic Retinopathy Medical Image Classification

Assessing the impact of nature-based solutions on soil health in sub-Saharan Africa through farmer-centred methods

Prizes and Awards

Research Overview

Research Specialisms

Current Research

Past Research

Collaborations

Funding and Grants

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

Bottom-up emissions and energy assessment for decommissioning offshore platform structures in the North Sea: Brent and Tern case studies

VisionTreeS: A Hybrid Tree-Based Visual Masked Autoencoder Approach for Strawberry Yield Forecasting From Low-Resolution Data

Personalised Multi-Task Federated Learning for Diabetic Retinopathy Medical Image Classification

Assessing the impact of nature-based solutions on soil health in sub-Saharan Africa through farmer-centred methods

Adaptive Quantum-Augmented Security Framework for Vehicle-to-Everything (AQAS-V2X) Communication

Pushing the Limits of Sparsity: A Bag of Tricks for Extreme Pruning

Food Standards Agency Science Council Report of Project ‘Artificial Intelligence Applications in Food Safety and Authenticity’

EquiCaps: Predictor-Free Pose-Aware Pre-Trained Capsule Networks

Capsule Network Projectors are Equivariant and Invariant Learners

OmniNet: An Expandable Causal Inference Network for Multiple Modalities

Stereotypical Bias Amplification, and Reversal, in an Experimental Model of Human Interaction with Generative AI

Response to UKIPO Consultation on Copyright and Artificial Intelligence

Masked Capsule Autoencoders

Exploring Segment Anything Foundation Models for Out of Domain Crevasse Drone Image Segmentation

Multimodal Federated Learning With Missing Modalities through Feature Imputation Network

Embedding AI-Enabled Data Infrastructures for Sustainability in Agri-Food: Soft-Fruit and Brewery Use Case Perspectives

Farm Explorer: A Tool for Calculating Transparent Greenhouse Gas Emissions

Accelerate Training of Personalised Multi-Task Federated Learning

LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations

The Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework: a proposed application of IDEAL principles to artificial intelligence applications in trauma and orthopaedics

Bottom-up formulations for the multi-criteria decision analysis of oil and gas pipeline decommissioning in the North Sea: Brent field case study

Object-Centric Learning with Capsule Networks: A Survey

Automatic segmentation of radar data from the Chang’E-4 mission using unsupervised machine learning: A data-driven interpretation approach

A Multi-Farm Global-to-Local Expert-Informed Machine Learning System for Strawberry Yield Forecasting

Model pruning enables localized and efficient federated learning for yield forecasting and data sharing

Specialist Advisory Group (SAG) on Applications of Artificial Intelligence to Environment Natural Resources & Agriculture Policy, Scottish Government - Workshop Report

Science in the age of AI: How artificial intelligence is changing the nature and method of scientific research

Semantic Positive Pairs for Enhancing Visual Representation Learning of Instance Discrimination methods

Automated Crevasse Mapping Using Deep Learning Foundation Models to Analyse Climate Change and Glaciology

LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations

Segment Anything Model (SAM) for Automatic Crater Detection

Masked Capsule Autoencoders

A flexible deep learning crater detection scheme using Segment Anything Model (SAM)

ProtoCaps: A Fast and Non-Iterative Capsule Network Routing Method

Preface

LLEDA—Lifelong Self-Supervised Domain Adaptation

Tabular Machine Learning Methods for Predicting Gas Turbine Emissions

S-JEA: Stacked Joint Embedding Architectures for Self-Supervised Visual Representation Learning

HMSN: Hyperbolic Self-Supervised Learning by Clustering with Ideal Prototypes

Decarbonising Our Food Systems: Contextualising Digitalisation For Net Zero

Premonition Net, a multi-timeline transformer network architecture towards strawberry tabletop yield forecasting

Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing

Attention-Based Deep Learning Methods for Predicting Gas Turbine Emissions

Deep learning techniques for in-core perturbation identification and localization of time-series nuclear plant measurements

EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting

Hyperspherically Regularized Networks for Self-Supervision

Machine learning for analysis of real nuclear plant data in the frequency domain

AI-enabled Safe and Efficient Food Supply Chain

Learning with Capsules: A Survey

Graph Neural Networks for Reservoir Level Forecasting and Draught Identification

The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector

Fully Homomorphically Encrypted Deep Learning as a Service

Detection and Localisation of Multiple In-core Perturbations with Neutron Noise-based Self-Supervised Domain Adaptation

Contrastive Domain Adaptation

Commentary to the Commission’s proposal for the “AI Act” – Response to selected issues

Deep Learning for Computer-Aided Diagnosis in Ophthalmology: A Review

An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of plant growth

How might technology rise to the challenge of data sharing in agri-food?

Lightweight deep learning models for detecting COVID-19 from chest X-ray images

Neutron Noise-based Anomaly Classification and Localization using Machine Learning

Multi-source domain adaptation for quality control in retail food packaging

Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments

Data Sharing and Interoperability for Data Trusts Workshop: Summary Report