"Westlake University is unique from every perspective. By joining Westlake University, I will be able to pursue my research dreams in a city where my career started."
Biography
Yaochu Jin obtained the BSc., MSc. and PhD degree all in automatic control from the Electrical Engineering Department, Zhejiang University, China, in 1988, 1991, and 1996, respectively, and the Dr.-Ing. from the Institute of Neuroinformatics, Ruhr-University Bochum, Germany in 2001. He is currently a Chair Professor of Artificial Intelligence with the School of Engineering, Westlake University. Before joining Westlake University, he was an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, Bielefeld University, Germany from 2021 to 2023, and a Surrey Distinguished Chair Professor in Computational Intelligence, University of Surrey, Guildford, U.K., from 2010 to 2021. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, and “Changjiang Distinguished Visiting Professor”, Northeastern University, China from 2015 to 2017. He is a Member of Academia Europaea and Fellow of IEEE.
History
2023
Chair Professor of Artificial Intelligence, School of Engineering, Westlake University
President-Elect of IEEE Computational Intelligence Society
Editor-in-Chief of Complex & Intelligent Systems
2021
Alexander von Humboldt Chair of Artificial Intelligence
Faculty of Technology, Bielefeld University, Germany
Member of Academia Europaea
2019
Surrey Distinguished Chair, Professor in Computational Intelligence, University of Surrey, Guildford, U.K.
2018
Distinguished Visiting Scholar, University of Technology, Sydney, Australia
2016
Editor-in-Chief of IEEE Transactions on Cognitive and Developmental Systems
2015
Finland Distinguished Professor, University of Jyväskylä, Jyväskylä, Finland
Changjiang Distinguished Visiting Professor, Northeastern University, China
Fellow, IEEE
2010
Chair of Computational Intelligence, Department of Computer Science, University of Surrey, UK.
1999
Scientist, Senior Scientist and Principal Scientist, Honda Research Institute Europe, Offenbach, Germany
2001
Dr.-Ing. from the Institute of Neuroinformatics, Ruhr University Bochum, Bochum, Germany
1998
Postdoctoral associate in the Department of Industrial Engineering, the State University of New Jersey, New Brunswick, NJ, USA.
1996
Ph.D. degree in Engineering, Zhejiang University, China
1991
Assistant Lecturer, Lecturer, and Associate Professor in the Department of Electrical Engineering, Zhejiang University, China
Master's Degree in Engineering, Zhejiang University, China
1988
Bachelor's degree in Engineering, Zhejiang University, China
Research
Prof Jin’s research interests include trustworthy artificial intelligence and artificial general intelligence. He has been working in the cross-disciplinary areas of computational intelligence, computational neuroscience and computational systems biology, such as evolutionary optimization and learning, secure and privacy-preserving machine learning and optimization, graph neural network based combinatorial optimization, pretrained generative models for optimization, spiking neural networks and neural plasticity, computational modeling of neural and morphological development, morphogenetic self-organizing swarm robots and reconfigurable modular robots, artificial life, and evolutionary developmental systems. He has published over 500 papers in IEEE Transactions and major conferences such as CVPR, NeurIPS, ICLR, and ACM MM. His research output has been applied to design optimization of many industrial systems, such as turbine engines, high-lift airfoils, vehicles, and aircraft fuselage, reverse engineering of biological gene regulatory networks, vaccine selection, healthcare, fintech and robotics. His research has been funded by EU FP7, UK EPSRC, UK Royal Society, German BMBF, NSF China, and several companies including Honda, Bosch, Airbus, Huawei and Nvidia.
Prof Jin is presently the President-Elect of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He was the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems, an IEEE Distinguished Lecturer in 2013-2015 and 2017-2019, the Vice President for Technical Activities of the IEEE Computational Intelligence Society (2015-2016). He is the recipient of the 2018, 2021 and 2023 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named as a “Highly Cited Researcher” consecutively from 2019 to 2022 by Clarivate. He is a Member of Academia Europaea and Fellow of IEEE.
Representative Publications
Monographs：
Y. Jin. Computational evolution of neural and morphological systems – Towards evolutionary developmental artificial intelligence. Springer, July 2023
Y. Jin, H. Zhu, J. Xu, and Y. Chen. Federated Learning: Fundamentals and Advances. Springer, Singapore, November 2022
Y. Jin, H. Wang, and C. Sun. Data-Driven Evolutionary Optimization. Springer, June 2021
Conference Papers:
P. Liao, Y. Jin* and Wenli Du. EMT-NAS: Transferring architectural knowledge between tasks from different datasets. The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (CVPR 2023), Vancouver, Canada, June 18-22, 2023
G. Xie, J. Wang, J. Liu, Y. Jin, and F. Zheng. Pushing the limits of fewshot anomaly detection in industry vision: Graphcore. The Eleventh International Conference on Learning Representations (ICLR 2023), Kigali, Rwanda, May 1-5, 2023
Y. Hong, Y. Jin*, and Y. Tang. Rethinking individual global max in cooperative multi-agent reinforcement learning. The Thirty-Sixth Annual Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, November 26 - December 4, 2022
G. Xie, J. Wang, Y. Huang, Y. Zheng, F. Zheng, and Y. Jin. FedMed-ATL: Misaligned unpaired brain image synthesis via affine transform loss. The 30th ACM International Conference on Multimedia (ACM MM 2022), 2022
Journal Papers:
F. Ntelemis, Y. Jin* and S. Thomas. A generic self-supervised framework of learning invariant discriminative features. IEEE Transactions on Neural Networks and Learning Systems, 2023 (accepted)
H. Zhu, X. Wang, and Y. Jin*. Federated many-task Bayesian optimization. IEEE Transactions on Evolutionary Computation, 2023 (accepted)
X. Wang, Y. Jin*, W. Du, and J. Wang. Evolving dual-threshold Bienenstock-Cooper-Munro learning rules in Echo State Networks. IEEE Transactions on Neural Networks and Learning Systems, 2022 (accepted)
Y. Xiao, Y. Jin*, and K. Hao. Adaptive prototypical networks with label words and joint representation learning for few-shot relation classification. IEEE Transactions on Neural Networks and Learning Systems, 34(3): 1406 – 1417, 2023
Q. Liu, Y. Jin*, M. Heiderich, and T. Rodemann. Coordinated Adaptation of Reference Vectors and Scalarizing Functions in Evolutionary Many-objective Optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(2): 763 – 775, 2022
F. Ntelemis, Y. Jin*, and S. A. Thomas. Image clustering using an augmented generative adversarial network and information maximization. IEEE Transactions on Neural Networks and Learning Systems, 33(12): 7461 – 7474, 2022
Q. Liu, Y. Jin*, M. Heiderich, T. Rodemann and G. Yu. An adaptive reference vector guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems. IEEE Transactions on Cybernetics, 52(5): 2698 – 2711, 2022
X. Wang, Y. Jin* and K. Hao. Computational modeling of structural synaptic plasticity in echo state networks. IEEE Transactions on Cybernetics, 52(10): 11254-11266, 2022
H. Zhu and Y. Jin*. Real-time federated evolutionary neural architecture search. IEEE Transactions on Evolutionary Computation, 26(2): 364-378, 2022
J. Xu, W. Du, Y. Jin*, W. He, and R. Cheng. Ternary compression for communication-efficient federated learning. IEEE Transactions on Neural Networks and Learning Systems, 33(3): 1162-1176, 2022
A. R. Shirazi and Y. Jin*. Regulated morphogen gradients for target surrounding and adaptive shape formation. IEEE Transactions on Cognitive and Developmental Systems, 13(4): 818-826, 2021
Y. Chen, X. Sun, and Y. Jin*. Communication-efficient federated deep learning with layer-wise asynchronous model update and temporally weighted aggregation. IEEE Transactions on Neural Networks and Learning Systems. 31(10): 4229 – 4238, 2020
H. Zhu and Y. Jin*. Multi-objective evolutionary federated learning. IEEE Transactions on Neural Networks and Learning Systems, 31(4): 1310-1322, 2020
Y. Jin*, H. Wang, T. Chugh, D. Guo, and K. Miettinen. Data-driven evolutionary optimization: An overview and case studies. IEEE Transactions on Evolutionary Computation, 23(3): 442-458, 2019
J. Ding, C. Yang, Y. Jin* and T. Chai. Generalized multi-tasking for evolutionary optimization of expensive problems. IEEE Transactions on Evolutionary Computation, 23(1): 44-58, 2019
T. Chugh, Y. Jin*, K. Miettinen, J. Hakanen, and K. Sindhya. A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Transactions on Evolutionary Computation, 21(1): 129-142, 2018
Contact Us
Email: jinyaochu@westlake.edu.cn
We have several open positions for research fellows, postdocs, PhD students and research assistants in the areas of trustworthy optimization and machine learning, evolving large language models, artificial life, computational modeling of neural plasticity, spiking neural networks, brain-body co-evolution, evolutionary and developmental robotics. Our lab is committed to training the next generation of scientists. We endeavor to provide an innovative, rigorous, and collegial research environment for our trainees, and offer continuous support for the career growth of young scientists.