Dr Georgios Leontidis

Biography

I am the University's Interdisciplinary Research Director for Data and AI, and a Reader in Machine Learning. 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 three funded projects, i.e. Enhancing Agri-Food Transparent Sustainability (EP/V042270/1), Predictive Emissions Monitoring System for Gas Turbines with Siemens Energy (EP/W522089/1) 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, before that a Senior Data Scientist at IBA Dosimetry in Germany and a Marie Curie FP7-ITN Fellow.

I am serving as programme committe in various top venues, such as NeurIPS, ICML, AAAI, ICLR, etc. 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 serving as External Examiner at Cranfield University (MSc applied AI) and University of Hull. I have also served as external panel member for programme validation events.

I have successfully supervised PhD students to completion and have served as PhD examiner several times.

I am currently supervising 12 PhD students.

Prizes and Awards

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

- Ranked at Top 6% of the EPSRC Full Peer Review College

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

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

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

Current Research

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

a) Detecting various types of perturbations via neutron noise modelling and deep learning. We are using simulated and real data for various types of nuclear reactors. The data are provided by our EU partners (EU-H2020, 2017-2021, http://cortex-h2020.eu/)

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 (https://www.ceh.ac.uk/our-science/projects/entrain) - 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 - EATS, 3-year EPSRC project with Unis of Nottingham, Dundee and SRUC (total grant value ~£1.1M of which ~£0.5M FEC for Aberdeen) - Co-I (EP/V042270/1) - 10/2021 to 09/2024
• Opening the black box: helping AI to persuade without bias - ESRC PhD studentship ~£47K - 10/2022 to 10/2026
• Machine Learning based Predictive Emissions Monitoring System for Gas Turbines, 4-year PhD studentship funded by iCASE EPSRC (~£89K) and Siemens Energy Industrial Turbomachinery Ltd (~£29,6K) - £118K - Principal Investigator (Primary Supervisor) - EP/W522089/1 - 10/2021 to 09/2025
• Machine Learning and Expert based system for soft fruit yield forecasting, 3-year PhD studentship funded by Data Lab and Angus Soft Fruits Ltd - £65K - Principal Investigator (Primary Supervisor) - 07/2021 to 06/2024
• Data Management and Interoperability for Data Trusts - Internet of Food Things EPSRC Network Plus - £50K – Principal Investigator (with Glasgow & Lincoln Unis and Upton Beach Consulting Ltd.) - 09/2020 to 03/2021
• NEXTGEN: Neural-network Encryption; eXploration of Techniques for secure aGricultural data processing – Internet of Food Things EPSRC Network Plus - £50K – Principal Investigator (Led by the Scotland’s Rural College) – 03/2020 to 09/2020
• Precision agriculture: AI- and Expert- based approach to forcast fruit production in high intra-field variation settings - BBSRC-CTP (awarded as PI in 2020 whilst at Uni of Lincoln: non-transferable studentship) - £102K, BB/V509784/1 - Co-I and external PhD supervisor
• Engineering Transformation for the Integration of Sensor Networks - Natural Environment Research Council (NERC) – £114K (FEC) –  Principal Investigator for Uni of Lincoln – (£340K FEC in total with Centre for Ecology and Hydrology) – 02/2019 to 06/2020 - NE/S016236/1, NE/S016244/1, NE/S016244/2
• BerryPredictor: Improving harvest forecasts, yield predictions and crop productivity by optimising zonal phytoclimates in covered strawberry production – Innovate UK – £80K - Co-I – 12/2019 to 11/2022 (whilst at Uni of Lincoln)
• CORe monitoring Techniques and Experimental validation and demonstration (Cortex) – EU H2020 - £155K - Co-Principal Investigator at Uni of Lincoln (~£5M in total, coordinated by Chalmers University of Technology, Sweden) – 09/2017 to 08/2021
• SmartGreen–Big Data and Eco-Innovative resource use in the NSR Greenhouse Industry – EU Interreg – £270K (+50% match funding) for UoL (~£1.7M total) –09/2017 to 08/2021 – Co-I
• The Development of Dynamic Energy Control Mechanisms for Food Retailing Refrigeration Systems – Innovate UK – £845K for Uni of Lincoln (~£3.5M in total) – 09/2016 to 11/2018 – Co-I (https://tinyurl.com/y9sj5tdp)
• ReACT Refrigeration AI Control Technologies – BBSRC seeding catalyst – 35,000£ - 10/2018 to 04/2019 – Co-I (Uni of Lincoln)
• The augmented agronomist: Synthesis of AI, ML and robotics to assist decision support. BBSRC-CTP-NPIF 4-year PhD Studentship (whilst at Uni of Lincoln) - National Institute of Agricultural Botany – £99,300 - 12/2018 to 11/2022 - Primary Supervisor (external)

Teaching Responsibilities

Course Coordinator: Data Mining & Visualisation - MSc Artificial Intelligence