Professor George Panoutsosy-Test
BEng(Hons), MSc, PhD, FHEA, MIET, MIEEE
School of Electrical and Electronic Engineering
Head of School
Professor of Computational Intelligence
Full contact details
School of Electrical and Electronic Engineering
Amy Johnson Building
Portobello Street
Sheffield
S1 3JD
- Profile
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George Panoutsos received his PhD degree in automatic control and systems engineering from the University of Sheffield, Sheffield, U.K, in 2007. He joined the Department of Automatic Control and Systems Engineering (University of Sheffield, UK) as a Lecturer in 2010, and promoted to Professor of Computational Intelligence in 2019.
George has a research grant portfolio of over £3M from the UK EPSRC, Innovate UK, DSTL, EU Horizon 2020 and direct industry funding, as well as over 100 research publications in theoretical as well as applied contributions in the areas of computational intelligence, data-driven modelling, optimisation, control, and decision support systems. In terms of applied research, the majority of his work is on advanced manufacturing systems, as well as healthcare applications, while also currently exploring research applications in energy and infrastructure.
- Research interests
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My research focuses on explainable and trustworthy machine learning (ML). Explainability is multifaceted in this context; I work on mathematical and computational methods in Computational Intelligence (CI) that enable enhanced understanding and transparent information use for neural networks, visual and numerical performance measures for many-objective optimisation algorithms, as well as linguistic interpretations of models, and safe control systems. Explainability and trustworthiness are key barriers in using machine learning in a range of critical applications, e.g. in engineering, and healthcare. A multitude of research questions still need to be addressed, for example how neural network - based systems learn and perform when information/data is imperfect, how can we exploit prior knowledge for enhanced learning, and how can we develop performance metrics that will allow us to understand the optimisation of systems at scale.
I welcome PhD applications in topics that fall under Computational Intelligence, in particular when these are concerned with explainable machine learning. Examples of recent PhD projects include, physics-guided neural networks, physics-guided generative models, new performance metrics for decomposition-based many-objective optimisation, information theoretic explainability in neural networks, safe reinforcement learning, and linguistic interpretations of Convolutional Neural Networks.
- Publications
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Journal articles
- Disturbance observer-based tracking control for roll-to-roll slot die coating systems under gap and pump rate disturbances.. CoRR, abs/2601.08488.
- Bridging simulation and practice in additive manufacturing: Reinforcement learning for electron beam melting control. Journal of Manufacturing Processes, 156(Part B), 121-135. View this article in WRRO
- The relative contributions of subjective and musical factors in music for sleep. PLOS One, 20(8). View this article in WRRO
- Surrogate-assisted optimization of roll-to-roll slot die coating. Scientific Reports, 15. View this article in WRRO
- TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models.. CoRR, abs/2507.10643.
- Testing on continuous production of mefenamic acids—Design of experiment through simulation and process optimisation. European Journal of Pharmaceutical Sciences, 210, 107102-107102.
- Self-driving laboratory platform for many-objective self-optimisation of polymer nanoparticle synthesis with cloud-integrated machine learning and orthogonal online analytics.. Polymer Chemistry, 16(12), 1355-1364. View this article in WRRO
- Information-theoretic sensor placement for large sewer networks. Water Research, 268(Pt B). View this article in WRRO
- Multi-layer process control in selective laser melting: a reinforcement learning approach. Journal of Intelligent Manufacturing. View this article in WRRO
- A novel pipeline employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy. Computers in Biology and Medicine, 181. View this article in WRRO
- Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning. Neural Computing and Applications, 36(30), 18841-18862. View this article in WRRO
- TabNet: Locally interpretable estimation and prediction for advanced proton exchange membrane fuel cell health management. Electronics, 13(7). View this article in WRRO
- Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach. 2024 IEEE International Conference on Industrial Technology (ICIT), 1-6.
- In-situ process control strategies for selective laser melting. IFAC-PapersOnLine, 56(2), 6594-6599. View this article in WRRO
- In-situ porosity prediction in metal powder bed fusion additive manufacturing using spectral emissions: a prior-guided machine learning approach. Journal of Intelligent Manufacturing, 35(6), 2719-2742. View this article in WRRO
- Stability in Reinforcement Learning Process Control for Additive Manufacturing. IFAC-PapersOnLine, 56(2), 4719-4724.
- Synchrotron imaging derived relationship between process parameters and build quality for directed energy deposition additively manufactured IN718. Additive Manufacturing Letters, 6.
Book chapters
- A Fuzzy Logic-Based Framework for Statistical Process Control in Additive Manufacturing, Advances in Intelligent Systems and Computing (pp. 61-72). Springer Nature Switzerland
- Improving the Explainability of Multi-criteria Decision-Making Using Neutrosophic Logic, Advances in Intelligent Systems and Computing (pp. 551-562). Springer Nature Switzerland
- Overlapping Granular Clustering: Application in Fuzzy Rule-Based Classification, Lecture Notes in Networks and Systems (pp. 84-93). Springer Nature Switzerland
Conference proceedings
- Interpretability Indices for Type-2 Fuzzy Logic Systems. 2025 6th International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp 7-11), 2 September 2025 - 3 September 2025.
- A New Framework for Visualising Diversity Properties of Pareto Front Approximations in Many-objective Optimisation. Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp 447-450)
- Constrained Reinforcement Learning for Advanced Control in Powder Bed Fusion*. 2025 European Control Conference (ECC) (pp 1828-1835), 24 June 2025 - 27 June 2025.
- Output Tracking for Uncertain Time-Delay Systems via Robust Reinforcement Learning Control. 2024 43rd Chinese Control Conference (CCC) (pp 2219-2226), 28 July 2024 - 31 July 2024.
- Reinforcement learning-based output stabilization control for nonlinear systems with generalized disturbances. 2024 IEEE International Conference on Industrial Technology (ICIT) (pp 1-6). Bristol, United Kingdom, 25 March 2024 - 25 March 2024. View this article in WRRO
- Initial investigation of online control system for selective laser melting process: multi-layer level. 2024 UKACC 14th International Conference on Control (CONTROL). Winchester, UK, 10 April 2024 - 10 April 2024. View this article in WRRO
- Disturbance observer-based optimal tracking control for slot coating process with mismatched input disturbances. 2024 UKACC 14th International Conference on Control (CONTROL). Winchester, UK, 10 April 2024 - 10 April 2024. View this article in WRRO
- A reinforcement learning-based approach for optimal output tracking in uncertain nonlinear systems with mismatched disturbances. 2024 UKACC 14th International Conference on Control (CONTROL) (pp 169-174). Winchester, United Kingdom, 10 April 2024 - 10 April 2024. View this article in WRRO
- Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach.. ICIT (pp 1-6)
- Advances in Computational Intelligence Systems - Contributions Presented at the 21st UK Workshop on Computational Intelligence, UKCI 2022, September 7-9, 2022, Sheffield, UK. UKCI, Vol. 1454
- Reinforcement Learning for Multiple-Input Multiple-Output Control in Metal Additive Manufacturing. 2023 IEEE International Conference on Networking, Sensing and Control (ICNSC) (pp 1-6), 25 October 2023 - 27 October 2023.
- High Dimensional Many Objective Optimisation through Diverse Creation and Categorisation of Reference Vectors. Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp 423-426)
Preprints
- Disturbance observer-based tracking control for roll-to-roll slot die coating systems under gap and pump rate disturbances, arXiv.
- TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models, arXiv.
- Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach, arXiv.
- A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy, arXiv.
- Disturbance observer-based tracking control for roll-to-roll slot die coating systems under gap and pump rate disturbances.. CoRR, abs/2601.08488.
- Grants
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Current Grants
- Diode area melting - a novel re-configurable multi-laser approach for efficient additive manufacturing with enhanced thermal process control, RCUK, 01/06/2022 - 30/11/2024
- NanoMan: Self-Optimising Nanoscale Manufacturing Platforms for Achieving Multiscale Precision, RCUK, 13/01/2022 - 12/01/2025
- Responsive Manufacturing of High Value Thin to Thick Films, RCUK, 01/09/2021 - 31/08/2024
- DAM: Developing Design for Additive Manufacturing, Innovate UK, 01/12/2018 - 30/11/2022
- Integradde, EU H2020, 01/10/2018 - 31/03/2023
- MAPP: EPSRC Future Manufacturing Hub in Manufacture using Advanced Powder Processes, RCUK, 01/10/2016 - 30/09/2023
Previous Grants
- AIRLIFT: Additive IndustrRiaLIsation for Future Technology), Innovate UK, 01/12/2018 - 30/11/2023
- Machine Learning digital twin for defect-free additive manufacturing, Research England, 01/02/2022 - 30/06/2022
- Materials 4.0, RCUK, 01/01/2022 - 31/03/2022
- Teaching activities
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- ELE420 Industrial training programme (ITP) in Advanced Manufacturing
- ELE428 Industrial Training Programme (ITP) in Computational Intelligence
- Professional activities and memberships
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- Co-founder of Phlux Technology