Professor George Panoutsos

BEng(Hons), MSc, PhD, FHEA, MIET, MIEEE

School of Electrical and Electronic Engineering

Head of School

Professor of Computational Intelligence

Professor George Panoutsos
Profile picture of Professor George Panoutsos
g.panoutsos@sheffield.ac.uk
Amy Johnson Building

Full contact details

Professor George Panoutsos
School of Electrical and Electronic Engineering
Amy Johnson Building
Portobello Street
Sheffield
S1 3JD
Profile

George Panoutsos received his PhD degree in automatic control and systems engineering from the ±¬ÁÏTV, Sheffield, U.K, in 2007. He joined the Department of Automatic Control and Systems Engineering (±¬ÁÏTV, 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

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.

Towards formulating research questions in machine learning, I often use challenge-driven research e.g. in manufacturing, healthcare, as case studies. This way,  applications drive the research questions, towards maximising impact. I also use explainable machine learning for translational research and to create innovation to address global challenges (e.g. sustainability, energy). The advanced monitoring, optimisation and control of manufacturing processes is such an example, where ML-based methods can be used to reduce material waste, and minimise energy use.

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

Journal articles

  • Tang Z, Passmore C, Campbell AI, Howse J, Rossiter JA, Ebbens S & Panoutsos G (2026) Disturbance observer-based tracking control for roll-to-roll slot die coating systems under gap and pump rate disturbances.. CoRR, abs/2601.08488.
  • Vagenas S, Boone N & Panoutsos G (2025) . Journal of Manufacturing Processes, 156(Part B), 121-135.
  • Kirk R, Panoutsos G, van de Werken M & Timmers R (2025) . PLOS One, 20(8).
  • Passmore C, Wu KE, Howse JR, Panoutsos G & Ebbens SJ (2025) . Scientific Reports, 15.
  • Tang Y, Esnaola I & Panoutsos G (2025) TaylorPODA: A Taylor Expansion-Based Method to Improve Post-Hoc Attributions for Opaque Models.. CoRR, abs/2507.10643.
  • Wu KE, Brown CJ, Robertson M, Johnston BF, Lloyd R & Panoutsos G (2025) . European Journal of Pharmaceutical Sciences, 210, 107102-107102.
  • Knox ST, Wu KE, Islam N, O'Connell R, Pittaway PM, Chingono KE, Oyekan J, Panoutsos G, Chamberlain TW, Bourne R & Warren NJ (2025) . Polymer Chemistry, 16(12), 1355-1364.
  • Crowley G, Tait S, Panoutsos G, Speight V & Esnaola I (2025) . Water Research, 268(Pt B).
  • Vagenas S, Al-Saadi T & Panoutsos G (2024) . Journal of Intelligent Manufacturing.
  • Mamalakis M, Macfarlane SC, Notley SV, Gad AKB & Panoutsos G (2024) . Computers in Biology and Medicine, 181.
  • Mamalakis M, Banerjee A, Ray S, Wilkie C, Clayton RH, Swift AJ, Panoutsos G & Vorselaars B (2024) . Neural Computing and Applications, 36(30), 18841-18862.
  • Zhang B, Jin X, Liang W, Chen X, Li Z, Panoutsos G, Liu Z & Tang Z (2024) . Electronics, 13(7).
  • Zhao H, Tang Z, Li Z, Dong Y, Si Y, Lu M & Panoutsos G (2024) . 2024 IEEE International Conference on Industrial Technology (ICIT), 1-6.
  • Al-saadi T, Rossiter JA & Panoutsos G (2023) . IFAC-PapersOnLine, 56(2), 6594-6599.
  • Atwya M & Panoutsos G (2023) . Journal of Intelligent Manufacturing, 35(6), 2719-2742.
  • Vagenas S & Panoutsos G (2023) . IFAC-PapersOnLine, 56(2), 4719-4724.

Book chapters

  • Sahin A, Rey P & Panoutsos G (2024) , Advances in Intelligent Systems and Computing (pp. 61-72). Springer Nature Switzerland
  • Yusuf H, Yang K & Panoutsos G (2024) , Advances in Intelligent Systems and Computing (pp. 551-562). Springer Nature Switzerland
  • Muda MZ & Panoutsos G (2024) , Lecture Notes in Networks and Systems (pp. 84-93). Springer Nature Switzerland

Conference proceedings

  • Muda MZ & Panoutsos G (2025) . 2025 6th International Conference on Artificial Intelligence and Data Sciences (AiDAS) (pp 7-11), 2 September 2025 - 3 September 2025.
  • Wu KE & Panoutsos G (2025) . Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp 447-450)
  • Vagenas S & Panoutsos G (2025) . 2025 European Control Conference (ECC) (pp 1828-1835), 24 June 2025 - 27 June 2025.
  • Tang Z, Rossiter JA, Jin X, Zhang B & Panoutsos G (2024) . 2024 43rd Chinese Control Conference (CCC) (pp 2219-2226), 28 July 2024 - 31 July 2024.
  • Tang Z, Rossiter JA, Dong Y & Panoutsos G (2024) . 2024 IEEE International Conference on Industrial Technology (ICIT) (pp 1-6). Bristol, United Kingdom, 25 March 2024 - 25 March 2024.
  • Al-saadi T, Rossiter JA & Panoutsos G (2024) . 2024 UKACC 14th International Conference on Control (CONTROL). Winchester, UK, 10 April 2024 - 10 April 2024.
  • Tang Z, Passmore C, Rossiter J, Ebbens S, Dunderdale G & Panoutsos G (2024) . 2024 UKACC 14th International Conference on Control (CONTROL). Winchester, UK, 10 April 2024 - 10 April 2024.
  • Tang Z, Rossiter JA & Panoutsos G (2024) . 2024 UKACC 14th International Conference on Control (CONTROL) (pp 169-174). Winchester, United Kingdom, 10 April 2024 - 10 April 2024.
  • Zhao H, Tang Z, Li Z, Dong Y, Si Y, Lu M & Panoutsos G (2024) Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach.. ICIT (pp 1-6)
  • (2024) 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
  • Grais EM, Notley SV & Panoutsos G (2023) . 2023 IEEE International Conference on Networking, Sensing and Control (ICNSC) (pp 1-6), 25 October 2023 - 27 October 2023.
  • Wu K & Panoutsos G (2023) . Proceedings of the Companion Conference on Genetic and Evolutionary Computation (pp 423-426)

Preprints

  • Tang Z, Passmore C, Campbell AI, Howse J, Rossiter JA, Ebbens S & Panoutsos G (2026) Disturbance observer-based tracking control for roll-to-roll slot die coating systems under gap and pump rate disturbances, arXiv.
  • Tang Y, Esnaola IA & Panoutsos G (2025) , arXiv.
  • Zhao H, Tang Z, Li Z, Dong Y, Si Y, Lu M & Panoutsos G (2024) , arXiv.
  • Mamalakis M, Macfarlane SC, Notley SV, Gad AKB & Panoutsos G (2023) , arXiv.
Grants

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, £629,880, as Co-PI
  • NanoMan: Self-Optimising Nanoscale Manufacturing Platforms for Achieving Multiscale Precision, RCUK, 13/01/2022 - 12/01/2025, £1,432,279, as Co-PI
  • Responsive Manufacturing of High Value Thin to Thick Films, RCUK, 01/09/2021 - 31/08/2024, £2,025,997, as Co-PI
  • DAM: Developing Design for Additive Manufacturing, Innovate UK, 01/12/2018 - 30/11/2022, £930,941, as Co-PI
  • Integradde, EU H2020, 01/10/2018 - 31/03/2023, £694,628, as Co-PI
  • MAPP: EPSRC Future Manufacturing Hub in Manufacture using Advanced Powder Processes, RCUK, 01/10/2016 - 30/09/2023, £6,776,372, as Co-PI

Previous Grants

  • AIRLIFT: Additive IndustrRiaLIsation for Future Technology), Innovate UK, 01/12/2018 - 30/11/2023, £545,174, as Co-PI
  • Machine Learning digital twin for defect-free additive manufacturing, Research England, 01/02/2022 - 30/06/2022, £29,551, as Co-PI
  • Materials 4.0, RCUK, 01/01/2022 - 31/03/2022, £54,647, as PI
  • CMAC Feasibility Study, RCUK, 01/10/2021 - 30/09/2022, £59,982, as PI
  • VULCAN, Innovate UK, 01/11/2019 - 31/01/2022, £334,563, as PI
  • Using Machine learning to enable feedback controlled manufacture of self-assembled patterned materials, RCUK, 30/09/2019 - 28/03/2022, £252,938, as Co-PI
  • TACDAM: Tailorable & Adaptive Connected Digital Additive Manufacturing, RCUK, 01/01/2017 - 31/12/2018, £221,611, as PI
  • MIRIAM: Machine Intelligence for Radically Improved Additive Manufacturing, Innovate UK, 01/10/2017 - 31/03/2019, £261,312, as Co-PI
  • Integrated machine-part multi-objective optimisation for powder manufacturing, RCUK, 01/11/2016 - 3103/2017, £40,000, as PI
Teaching activities
  • ELE420 Industrial training programme (ITP) in Advanced Manufacturing
  • ELE428 Industrial Training Programme (ITP) in Computational Intelligence

Links

  Information and Communication Engineering