Professor George Panoutsos
Faculty Director for Research and Innovation - Faculty of Engineering
Professor of Computational Intelligence
Professor George Panoutsos BEng(Hons), MSc, PhD, FHEA
Department of Automatic Control and Systems Engineering
University of Sheffield
Sheffield, S1 3JD
Tel: (+44) (0)114 222 5130
Fax: (+44) (0)114 222 5683
PA: Emma Cooper
Tel: +44 (0)114 222 2163
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 £2M from the UK EPSRC, Innovate UK, EU Horizon 2020 and direct industry funding, as well as over 60 research publications in theoretical as well as applied contributions in the areas of computational intelligence, data-driven modelling, optimisation, 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 infrastructure.
- Interpretable Machine Learning
- Computational Intelligence (CI) and Artificial Intelligence (AI)
- Granular Computing (GrC) and Computing with Words
- Incremental learning – Smart Adaptive Systems
- Data mining, classification and pattern recognition
- Manufacturing systems
- Process monitoring and optimisation, decision making
- Data-driven process-part quality assurance and forecasting of defects, manufacturing informatics
- applications: additive manufacturing (3D printing), laser-based welding and cladding, friction stir welding, steel making
- Biomedical intelligent systems and Decision Support in healthcare
- Decision support and data-driven modelling
- Personalised therapy, therapy optimisation
- applications: wearable devices, sleep disorders, cancer prognosis, ICU decision support
Recent research awards and projects
- 2019-2021 EPSRC, Using Machine learning to enable feedback controlled manufacture of self-assembled patterned materials (co-I £250k)
- 2019-2022, Aerospace Technology Institute, DAM, Developing Design for Additive Manufacturing, (co-I, ACSE project £186k)
- 2019-2022, Aerospace Technology Institute, AIRLIFT, Additive IndustRiaLIsation FuTure Technology, (co-I, ACSE project £140k)
- 2016-2022 EPSRC MAPP Hub, Future Powder Manufacturing Hub (co-I £10M)
- 2018-2020 EU H2020, INTEGRADDE (co-I £12.7M)
- 2017-2019 Innovate UK, MIRIAM - Machine Intelligence for Radically Improved Additive Manufacturing (co-I £666k)
- 2016 -2018 Innovate UK, TACDAM, Tailorable & Adaptive Connected Digital Additive Manufacturing (academic PI £1M )
- 2018-2020 TWI Ltd, Phased array NDT in Stir Welding: Interpretable machine learning and process monitoring (PI £42k)
- 2015-2017 EU H2020, Factories of The Future - 01: Process Optimisation of Manufacturing Assets, COMBILASER, (co-I and academic lead, £3.48M)
- 2014-2016 TSB, Sustained Process Excellence through Embedding of Analytics and Knowledge Management into Process Chains, Academic (PI, total project cost £441k)
- 2013-2014 METRC Innovation Award, Online and real-time condition monitoring of Friction Stir Welding, (PI £10k)
- 2012 EPSRC/Sheffield University, Model-based performance evaluation for critical manufacturing processes (PI £61k)
- 2012-2014 TWI Ltd. Yorkshire, UK, Automated Systems for Intelligent Stir Tracking and Optimisation (PI £29k)
- 2010-2013 TWI Ltd. Cambridge, UK, Multiscale model-based search for optimal Process Operating Windows in Friction Stir Welding (PI £6k)
- ACS6101, Foundations of Control Systems, module leader
- ACS6501, Foundations of Robotics (teaching: systems modelling part)
- ACS6402, Industry Training Programme: Advanced Manufacturing (module leader)
- ACS6403, Industry Training Programme: Computational Intelligence (module leader)
- Snell R, Tammas-Williams S, Chechik L, Lyle A, Hernández-Nava E, Boig C, Panoutsos G & Todd I (2019) Methods for Rapid Pore Classification in Metal Additive Manufacturing. JOM, 1-9. View this article in WRRO
- Baraka A & Panoutsos G (2019) Long-term Learning for Type-2 Neural-Fuzzy Systems. Fuzzy Sets and Systems, 368, 59-81. View this article in WRRO
- Shi C, Panoutsos G, Luo B, Liu H, Li B & Lin X (2019) Using Multiple-Feature-Spaces-Based Deep Learning for Tool Condition Monitoring in Ultraprecision Manufacturing. IEEE Transactions on Industrial Electronics, 66(5), 3794-3803. View this article in WRRO
- Rubio-Solis A, Melin P, Martinez-Hernandez U & Panoutsos G (2019) General Type-2 Radial Basis Function Neural Network: A Data-Driven Fuzzy Model. IEEE Transactions on Fuzzy Systems, 27(2), 333-347. View this article in WRRO
- Panoutsos G, Baraka A & Cater S (2015) A real-time quality monitoring framework for steel friction stir welding. Journal of Manufacturing Processes, 20(1), 137-148.
- Baraka A, Panoutsos G & Cater S (2015) A real-time quality monitoring framework for steel friction stir welding using computational intelligence. Journal of Manufacturing Processes, 20(P1), 137-148.
- Zhang Q, Mahfouf M, Panoutsos G, Beamish K & Liu X (2015) Multiobjective optimal design of friction stir welding considering quality and cost issues. Science and Technology of Welding and Joining, 20(7), 607-615. View this article in WRRO
- De Alejandro Montalvo J, Panoutsos G, Mahfouf M & Catto JW (2015) High Dimensionality and Scaling-up Performance of RBF Models with Application to Healthcare Informatics. International Journal of Machine Learning and Computing, 5(1), 62-67.
- Solis AR & Panoutsos G (2014) Interval Type-2 Radial Basis Function Neural Network: A Modeling Framework. IEEE Transactions on Fuzzy Systems, 23(2), 457-473.
- Solis AR & Panoutsos G (2013) Granular computing neural-fuzzy modelling: A neutrosophic approach. APPLIED SOFT COMPUTING, 13(9), 4010-4021.
- Samuri SM, Panoutsos G, Mahfouf M, Mills GH, Denai M, Mills GH & Brown BH (2013) Towards a Patient-Specific Model of Lung Volume Using Absolute Electrical Impedance Tomography (aEIT), 273, 191.
- Zhang Q, Mahfouf M, Panoutsos G, Beamish K & Norris I (2012) Knowledge discovery for friction stir welding via data driven approaches part 1 - Correlation analyses of internal process variables and weld quality. Science and Technology of Welding and Joining, 17(8), 672-680.
- Zhang Q, Mahfouf M, Panoutsos G, Beamish K & Norris I (2012) Knowledge discovery for friction stir welding via data driven approaches Part 2 - Multiobjective modelling using fuzzy rule based systems. Science and Technology of Welding and Joining, 17(8), 681-693.
- Yang YY, Mahfouf M & Panoutsos G (2011) Probabilistic characterisation of model error using Gaussian mixture model-With application to Charpy impact energy prediction for alloy steel. Control Engineering Practice.
- Samuri SM, Panoutsos G, Mahfouf M, Mills GH, Denaï M & Brown BH (2011) Towards a Patient-Specific Model of Lung Volume Using Absolute Electrical Impedance Tomography (aEIT). Communications in Computer and Information Science, 273, 191-204.
- Zhang Q, Mahfouf M, Yates JR, Pinna C, Panoutsos G, Boumaiza S, Greene RJ & de Leon L (2011) Modeling and Optimal Design of Machining-Induced Residual Stresses in Aluminium Alloys Using a Fast Hierarchical Multiobjective Optimization Algorithm. Materials and Manufacturing Processes, 26(3), 508-520.
- Wang A, Mahfouf M, Mills GH, Panoutsos G, Linkens DA, Goode KM, Kwok H-F & Denaï MA (2010) Intelligent model-based advisory system for the management of ventilated intensive care patients. Part II: Advisory system design and evaluation.. Computer Methods and Programs in Biomedicine, 99, 208-217.
- Ting CH, Mahfouf M, Nassef A, Linkens DA, Panoutsos G, Nickel P, Roberts AC & Hockey GRJ (2010) Real-time adaptive automation system based on identification of operator functional state in simulated process control operations. IEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans, 40(2), 251-262.
- Panoutsos G & Mahfouf M (2010) A neural-fuzzy modelling framework based on granular computing: Concepts and applications. Fuzzy Sets and Systems, 161(21), 2808-2830.
- Rubio-Solis A, Baraka A, Panoutsos G & Thornton S (2018) Data-Driven Interval Type-2 Fuzzy Modelling for the Classification of Imbalanced Data, Studies in Systems, Decision and Control (pp. 37-51).
- Panoutsos G & Mahfouf M (2008) An incremental learning structure using granular computing and model fusion with application to materials processing (pp. 139-153).
Conference proceedings papers
- Rubio-Solis A, Martinez-Hernandez U & Panoutsos G (2018) Evolutionary Extreme Learning Machine for the Interval Type-2 Radial Basis Function Neural Network: A Fuzzy Modelling Approach. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 8 July 2018 - 13 July 2018. View this article in WRRO
- Martinez-Hernandez U, Rubio-Solis A, Panoutsos G & Dehghani-Sanij AA (2017) A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors. IEEE International Conference on Fuzzy Systems View this article in WRRO
- Rubio-Solis A & Panoutsos G (2017) An ensemble data-driven fuzzy network for laser welding quality prediction. 2017 IEEE International Conference on Fuzzy Systems, 9 July 2017 - 12 July 2017. View this article in WRRO
- Huse M, Panoutsos G, Emde B, Rubio Solis A, Hermsdorf J & Kaierle S (2017) Closing the loop – Using Online Monitoring Techniques for an Automated Laser Welding Process Optimization in Industrial Applications. Proceedings of the Lasers in Manufacturing Conference 2017, 26 June 2017 - 29 June 2017.
- Baraka A, Panoutsos G & Cater S (2016) Perpetual Learning Framework based on Type-2 Fuzzy Logic System for a Complex Manufacturing Process. IFAC-PapersOnLine, Vol. 49(20) (pp 143-148) View this article in WRRO
- Solis AR & Panoutsos G (2016) Iterative Information Granulation for Novelty Detection in Complex Datasets. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 24 July 2016 - 29 July 2016. View this article in WRRO
- Tzagarakis G & Panoutsos G (2016) Model-Based Feature Selection Based on Radial Basis Functions and Information Measures. 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 24 July 2016 - 29 July 2016. View this article in WRRO
- Gonzalez-Rodriguez A, Panoutsos G, Mahfouf M & Beamish K (2014) A Novelty detection framework based on fuzzy entropy for a complex manufacturing process. IEEE International Conference on Intelligent Systems 2014, IEEE IS'14. Warsaw, Poland, 24 September 2014 - 26 September 2014.
- Baraka A, Gonzalez-Rodriguez AA, Panoutsos G, Beamish K & Cater S (2014) Manufacturing Informatics and Human-in-the-loop: A case study on Friction Stir Welding. 3rd EPSRC Manufacturing the Future Conference. Glasgow, 23 September 2014 - 24 September 2014.
- Baraka A, Panoutsos G, Mahfouf M & Cater S (2014) A Shannon entropy-based conflict measure for enhancing granular computing-based information processing. 2014 IEEE International Conference on Granular Computing (GrC), 22 October 2014 - 24 October 2014.
- Solis, A.R. & Panoutsos G (2014) Fuzzy uncertainty assessment in RBF Neural Networks using neutrosophic sets for multiclass classification. IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2014. Beijing, China, 6 July 2014 - 11 July 2014.
- De Alejandro Montalvo J, Panoutsos G, Mahfouf M & Catto JW (2013) Radial basis function neural-fuzzy model for microarray signature identification. BIOINFORMATICS 2013 - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (pp 134-139)
- Panoutsos G, Mumtaz, K. & Ghadbeigi, H. (2013) Systematic modelling and real-time optimisation for manufacturing complex geometries using additive manufacturing technologies. 2nd Annual EPSRC Manufacturing the Future conference. Cranfield University, UK, 17 September 2013 - 18 September 2013.