Professor Yaochu Jin holds a Chair in Computational Intelligence and heads the
Nature Inspired Computing and Engineering (NICE) Group ,
Department of Computing, University of Surrey, Guildford, Surrey, UK. He is also a "Finland Distinguished Professor" at Univerisity of Jyvaskyla, Finland, "Changjiang Distinguished Professor" at Northeastern University, China, and Visiting Professor at Donghua University, China.
Professor Jin is Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems, and Editor-in-Chief of Springer's free Open Access Journal Complex & Intelligent Systems . He is a Fellow of IEEE.
He received the BSc, MSc and PhD degrees, all in automatic control from Zhejiang University, China and the Dr.-Ing. degree from Ruhr-University Bochum, Germany. He was Principal Scientist and Group Leader at the Honda Research Institute Europe, Germany before he was appointed Chair in Computational Intelligence at University of Surrey in June 2010. He was Associate Lecturer, Lecturer and Associate Professor with the Electrical Engineering Department, Zhejiang University from 1991 to 1996.
He has published over 200 journal and conference papers and was granted 9 US/EU/Japan patents. His papers have reported over 12,000 citations (according to Google Scholar). Since he joined Surrey in June 2010, he has successfully attracted fundings from EU FP7, UK EPSRC, and industries, including Santander, Bosch UK, HR Wallingford, Intellas UK Ltd, Aero Optimal and Honda. He is also a member of the European GARTEUR Action Group "Surrogate-based Global Optimization Methods in Aerodynamic Design".
He was Vice President for Technical Activities of the IEEE Computational Intelligence Society (2014-2015) and a Distinguished Lecturer (2013-2015, 2017-2019) of IEEE. He was an AdCom member (2012-2013), a member of the Award Committee (2012-2013) and Chair (2011-2012) of the Intelligent Systems Applications Technical Committee of the IEEE Computational Intelligence Society. He was the founding General Co-Chair of the 2007, 2009, 2011 and 2013 IEEE Symposium on Computational Intelligence in Multi-Criterion Decision-Making, and the founding General Co-Chair of 2011 and 2013 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. He is the General Chair of the 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. He has delivered over 14 Keynote speeches at international conferences on morphogenetic robotics and morphogenetic self-organizing systems, developmental neural computation, modeling and analysis of gene regulatory networks, evolutionary optimization in dynamic and uncertain environments and multi-objective machine learning.
He also serves as an Associate Editor of BioSystems, the IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, the IEEE Transactions on Nanobioscience, the Soft Computing Journal, and Natural Computing. He is an Editorial Board Member of the Evolutionary Computation Journal (since 2014). He is a past Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, the IEEE Transactions on Systems, man and Cybernetics, Part C: Applications and Reviews, and the IEEE Transactions on Control Systems Technology.
Professor Jin is a member of Peer Review College and Review Panelist of EPSRC, UK, a member of Computer Science Review Panel, Academy of Finland, an Evaluator/Panel member of EU FP7 FET / Human Brain Project, a Peer Reviewer of Italian Evaluation of Research Quality exercise (VQR 2004-2010), and a Grant reviewer for many funding agencies, including the Royal Academy of Engineering (UK), the Levehulme Trust (UK), the Marsden Fund (New Zealand), Killam Fellowship Program (Canada), the Netherlands Organization for Scientific Research, Czech Science Foundation, Cyprus Research Promotion Fundation, FWF (Austria), and the European Science Foundation.
Recently Funded Projects (since 06/2010)
- "Many-objective optimisation for vehicle dynamics" funded by Honda R&D Europe (PI, 10.2017-09.2019)
- "Multi-objective evolutionary methods for hierarchical and multi-label classification" funded by FAPESP SPRINT (Surrey-PI, 01.2017-12.2018, PI: Dr Ricardo Cerri, UFSCar, Brazil)
- "Deep learning in mass spectrometry imaging", funded by EPSRC Industrial CASE Award (PI, 04/2017 - 03.2021)
- "Preference learning in decision making", funded by Honda Research Institute Europe (PI, 10/2016 - 09/2019)
- "Data-driven surrogate-assisted evolutionary fluid dynamic optimisation", funded by EPSRC (PI, Grant No. EP/M017869/1,06.2015 - 05.2018)
- SWARM-ORGAN: A theoretical framework for swarms of GRN-controlled agents which display adaptive tissue-like organisation", EU FP7 (PI at Surrey, in collboration with CRG, Spain, University of Amsterdam, the Netherlands, and University of East Anglia, UK, 03.2013-08.2016)
- "Sparse multi-way digital signal processing approach for
detection of deep medial temporal discharges from scalp EEG", funded by
EPSRC (Co-I, 01.2013-06.2015. PI: Dr Saeid Sanei)
- "Model-based evolutionary many-objective optimisation" funded by Honda Research Institute Europe (PI, 01.2013-12.2015)
- "Evolutionary optimisation of CFRP stiffened panels of aircraft", funded by EPSRC KTA and Aero Optimal (PI, 09.2011-09.2012)
- "Fast and robust feature extraction for detection of bootlegged museum images", funded by EPSRC CASE and Intellas UK Ltd (PI, 01.2011-06.2014)
- "Evolutionary methods for generating hierarchical and multi-label classifiers", Santander Doctoral Student Award, (PI, 03.2012-08.2012)
- "Reconstruction of global regulatory networks governing the production of antibiotics in Streptomyces bacteria", funded by EPSRC DTC, in collaboration with Microbial and Cellular Sciences Department, University of Surrey (PI, 07.2011-03.2014)
- "Heterogeneous ensenbles for convergence prediction in surrogate-asssited evolutionary optimisation", funded by EPSRC DTC, incollaboration with Division of Mechanical, Medical and Aerospace Engineering, University of Surrey (PI, 07.2011-03.2014)
- "Modeling neural plasticity for spatiotemporal pattern recognition", funded by EPSRC DTG (PI, 07.2011-12.2014)
- "Intelligent heat solutions: Concepts and strategies for product development", funded by EPSRC EngD and Bosch Thermotechnology (PI, 09.2011-08.2014)
- "Knowledge extraction and development of decision support systems for conceptual design of sustainable liquified nitrogen gas terminals", funded by EPSRC EngD and HR Wallingford (Co-I, 05.2011-04.2014)
- "Predicting suitable vaccines for foot-and-mouth disease virus based on genetic sequence", jointly funded by Microbial and Cellular Sciences Department and Computing Department, University of Surrey, in collaboration with Institute for Animal Health, Surrey (PI, 10.2011-09.2014)
- "Semi-supervised ensemble learning for BCI-based game control", funded by Department of Computing, University of Surrey (PI, 10.2011-09.2014)
- "Computational modeling of neural plasticity", start-up grant, funded by Department of Computing, University of Surrey (PI, 11.2010-10.2012)
I am particularly interested in nature-inspired machine intelligence and real-world driven problem-solving. Related research topics include:
My science-driven research interests lie in interdisciplinary areas that bridge the gap between computational intelligence and machine learning, computational neuroscience, and computational biology. My current main topics include
- Evolutionary optimization
- Data driven evolutionary optimization and decision-making
- Surrogate-assisted evolutionary optimization
- Robust and dynamic optimization, robustness over time
- Multi-objective and many-objective optimization
- Large-scale optimization
- Machine learning
- Multi-objective machine learning (accuracy versus complexity, diversity, sparsity, and interpretability)
- Secure machine learning (robust and resilient machine learning against adversarial behaviors, learning with encrypted data, and cybersecurity intelligence).
- Advanced machine learning (semi-supervised learning, transfer learning and knowledge incorporation, active learning, and ensemble learning)
- Deep learning (explainable deep learning, generative adversarial networks, and deep reinforcement learning)
- Ensemble learning and drop-out learning
- Real world applications include
- Design optimization and control of complex systems, e.g., wing high-lift systems, fuselage of aircraft, turbine engines and vehicles; decision support systems, such as natural gas terminal design, intelligent heating systems design, energy management in buildings;
- Process optimization and control, including steel-making and continuous casting, and control of multi-level carbon fibre stretching processes; electric power transmission systems
- Image processing and image identification
- Healthcare and bioinformatics
- Evolutionary developmental systems
- Neural and morphological development
- Gene regulatory networks
- Brain-body co-evolution
- Computational modeling of neural plasticity
- Computational modeling of plasticity
- Gene regulated plasticity in reservoir computing such as echo-state networks and liquid state machines
- Morphogenetic robotics
- Morophogenetic swarm robots
- Mophogenetic reconfigurable modular robotics