• Brain Inspired Computing/Intelligence 

• Neuromorphic Engineering 

• Spiking Neural Networks 

• Network Compression 

• AI Chips

· Doctor of Philosophy, Nanyang Technological University, School of  Electrical and Electronic Engineering, Singapore,  07/2007-08/2011. 

· Master of Engineering, Xi’an Jiaotong University, School of Electronic and  Information Engineering, P.R. China, 09/2004-07/2007. 

· Bachelor of Engineering, Xi’an University of Technology, Automation, P.R. China, 09/2000-07/2004. 

l2022.03~Now: Professor, Institute of Automation, Chinese Academy of Sciences 

l2022.03~Now: Professor, The University of Chinese Academy of Sciences, China 

l2017.12~2022.02:  Associate Professor, Tsinghua University, China  

l2014.03~2017.11: Assistant Professor, Tsinghua University, China  

l2011.08~2014.07: Scientist,  Agency for Science, Technology and Research, A*STAR, Singapore. 

Journal Papers

[1] Z.Jia, K.You, W. He, Y. Tian, Y. Feng, Y. Wang, X.Jia, Y. Lou, J. Zhang, G. Li and Z. Zhang Event-Based Semantic Segmentation With Posterior Attention, IEEE Transactions on Image Processing, vol.32,1829-1842, 2023.

[2] Y. Du, M. Shi, F. Wei, G. Li, Boosting Zero-shot Learning via Contrastive Optimization of Attribute Representations, IEEE Transactions on Neural Networks and Learning Systems, Accepted, 2023

[3] J.Liu， Y.Hu，G.Li， J. Pei and L. Deng, Spike Attention Coding for Spiking Neural Networks,IEEE Transactions on Neural Networks and Learning Systems, Accepted, 2023.

[4] J. Ding, Y. Zhang, K. Song, G. Li, W. Wang, K. Liu, Target Controllability of Multiplex Networks with Weighted Interlayer Edges, IEEE Transactions on Network Science and Engineering, Accepted, 2023

[5]  M.Yao, G. Zhao and G. Li* et al, Sparser Spiking Activity can be Better: Feature Refine-and-Mask Spiking Neural Network for Event-based Visual Recognition, Neural Networks, Accepted, 2023.

[6] Y. Zhang, J. Ding, K. Song, G. Li,W. Wang and K. Liu, Target Controllability of Multiplex Networks with Weighted Interlayer Edges, IEEE Transactions on Network Science and Engineering, Accepted, 2023.

[7] M.Yao, G. Zhao, H. Zhang, Y. Hu, L. Deng, Y. Tian, B. Xu, G. Li*, Attention spiking neural networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted, 2023.

[8] T. Yan, G. Wang, T. Liu, G. Li, C. Wang, S. Funahashi, D. Suo and G Pei, Effects of Microstate Dynamic Brain Network Disruption in Different Stages of Schizophrenia, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Accepted, 2023.

[9] Y. Ma, R. Huang, M. Yan, Guoqi Li, Tian WangAttention-based Local Mean K-Nearest Centroid Neighbor Classifier,

Expert Systems with Applications, Accepted, pp. 117159，2023.

[10] Y. Zhao, Y. Song, G. Li, L. Deng, Y. Bai, X Wu, Multi-layer Rotation Memory Model-based correlation filter for visual tracking, Frontiers in Physics 10, 1003517,2022.

[11] Y. Ma, Y. Zhang, A. K. Sangaiah, M. Yan, G. Li and T. Wang, Active learning for name entity recognition with external knowledge, ACM Transactions on Asian and Low-Resource Language Information Processing

[12] D. Zhang, S. Liu, J. Zhang, G. Li, D. Suo, T. Liu, J. Luo, Z. Ming, J. Wu and T. Yan, Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator, IEEE Journal of Biomedical and Health Informatics, 26 (12), 6138-6149, 2022.

[13] Z. Chen, L. Deng, B. Wang, G. Li* and Y. Xie, A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.44, pp.13-31,2022.

[14] S. Ma, J. Pei, W. Zhang, G. Wang, D. Feng, F. Yu, C. Song, H. Qu, C. Ma, M. Lu, F. Liu, W. Zhou, Y. Wu, Y. Lin, H. Li, T. Wang, J. Song, X. Liu, G. Li, R. Zhao, L. Shi, Neuromorphic computing chip with spatiotemporal elasticity for multi-intelligent-tasking robots, Science Robotics , vol.7 (67), 2022

[15] R. Zhao, Z. Yang, H. Zheng, Y. Wu, F. Liu, Z. Wu, L. Li, F. Chen, S. Song, J. Zhu, W. Zhang, H. Huang, M. Xu, K. Sheng, Q. Yin, J. Pei, G. Li, Y. Zhang, M. Zhao, L. Shi , A framework for the general design and computation of hybrid neural networks, Nature Communications 13 (1), 1-12, 2022.

[16] Y. Wu, R. Zhao, J. Zhu, F. Chen, M. Xu, G. Li, S. Song, L. Deng, G. Wang, H. Zheng, S. Ma, J. Pei, Y. Zhang, M. Zhao, L. Shi, Brain-inspired global-local learning incorporated with neuromorphic computing, Nature Communications, vol. 1, pp.1 -14, 2022.

[17] Z. Wu, H Zhang, Y. Lin, G. Li*, M. Wang and Y. Tang, LIAF-Net: Leaky Integrate and Analog Fire Network for Lightweight and Efficient Spatiotemporal Information Processing, IEEE Transactions on Neural Networks and Learning Systems, vol.33, pp. 6249-6262, 2022.

[18] Y. Lin, Y. Hu, S. Ma, D. Yu, and G. Li*, Rethinking pretraining as a bridge from ANNs to SNNs. IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2022.3217796 , Accepted, 2022.

[19] B. Xu, F. Guo, W. Zhang, G. Li and C. Wen, E2DNet: An Ensembling Deep Neural Network for Solving Nonconvex Economic Dispatch in Smart Grid, IEEE Transactions on Industrial Informatics, Vol. 18 (5), pp. 3066-3076, 2022.

[20] Y. Lin, J. Sun, G. Li*, G. Xiao, C. Wen, L. Deng, H. E. Stanley, Spatiotemporal input control: leveraging temporal variation in network dynamics, IEEE/CAA Journal of Automatica Sinica, vol. 9 (4), pp. 635-651, 2022

[21] J. Sun, Y. Hao, C. Wen, J. Huang, G. Li*, Optimal Control of Temporal Networks with Variable Input and Node–Source Connection, IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2022.3193149, Accepted, 2022.

[22] Y. Zhao, Y. Song, G. Li, Y. Huang, Y. Bai, Y. Zhou, Q. Hao, CoGANet: Co-Guided Attention Network for Salient Object Detection, IEEE Photonics Journal, vol. 14 (4), pp. 1-12, 2022

[23] Y. Wu, D. Wang, X. Lu, F. Yang, W. Dong, J. Shi, G. Li*, Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions, Machine Intelligence Research, 2022.

[24] Y. Tian, G. Li*, P. Sun*, Information evolution in complex networks, Chaos: An Interdisciplinary Journal of Nonlinear Science , vil. 32 (7), 073105, 2022

[25] Y. Ma, R. Huang, M. Yan, G. Li, T. Wang, Attention-based Local Mean K-Nearest Centroid Neighbor Classifier, Expert Systems with Applications,  vol.201, 117159, 2022

[26] X. Liu, M. Yan, L. Deng, Y. Wu, D. Han, G. Li, X. Ye, and D. Fan. General spiking neural network framework for the learning trajectory from a noisy mmwave radar. Neuromorphic Computing and Engineering, vol. 2(3):034013, 2022.

[27] Y. Zhao, Y. Song, G. Li, L. Deng, Y. Bai, and X. Wu. Multi-layer rotation memory model-based correlation filter for visual tracking. Frontiers in Physics, DOI:10:1003517,  Accepted, 2022.

[28] Y. Tian, Z. Tan, H. Hou, G. Li, A. Cheng, Y. Qiu, K Weng, C. Chen, P. Sun, Theoretical foundations of studying criticality in the brain, Network Neuroscience, pp. 1-63,2022

[29] Y. Yang, X. Chi, L. Deng, T. Yan, F. Gao and G. Li*,Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization, Neurocomputing, vol. 511, pp. 175-186, 2022.

[30] D. Zhang, S. Liu, J. Zhang, G. Li, D. Suo, T. Liu, J. Luo, Z. Ming, J. Wu and T. Yan, Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator, IEEE Journal of Biomedical and Health Informatics, vol.26 (12), pp. 6138-6149, 2022.

[31] Y. Lin, J. Sun, G. Li*, G. Xiao, C. Wen, L. Deng, HE Stanley, Spatiotemporal input control: leveraging temporal variation in network dynamics, IEEE/CAA Journal of Automatica Sinica , vol. 9 (4), pp. 635-651, 2022.

[32] D Wang, G Zhao, H Chen, Z Liu, L Deng, G Li*, Nonlinear tensor train format for deep neural network compression, Neural Networks, vol.144, pp.320-333,2022

[33] Z. Wu, Z. Zhang, H. Gao, J. Qin, R Zhao, G. Zhao, G. Li*, Modeling learnable electrical synapse for high precision spatio-temporal recognition, Neural Networks, vol.149, pp. 184-194,2022 

[34] 陈叶飞， 赵广社， 李国齐*， 王鼎衡, 无监督多重非局部融合的图像去噪方法,自动化学报 48 (1), 87-102, 2022

[35] L. Liang, Z. Qu, Z. Chen, F. Tu, Y. Wu, L. Deng, G. Li, P. Li, Y. Xie, H2learn: High-efficiency learning accelerator for high-accuracy spiking neural networks, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, DOI: 10.1109/TCAD.2021.3138347, Accepted, 2021

[36] L. Deng, Y. Wu, Y. Hu, L. Liang, G. Li*, X. Hu*, Y. Ding, P. Li and Y. Xie， Comprehensive SNN, Compression Using ADMM Optimization and Activity Regularization, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS. 2021.3109064 , Accepted, 2021.

[37] L. Ling, X. Hu, L. Deng, Y. Wu, G. Li, Y. Ding and Y. Xie, Exploring Adversarial Attack in Spiking Neural Networks with Spike-Compatible Gradient, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3106961, Accepted, 2021.

[38] D. Wang, B. Wu, G. Zhao, M. Yao, H. Chen, L. Deng, T. Yan and G. Li*, Kronecker CP Decomposition with Fast Multiplication for batch Compressing RNNs, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3105961, Accepted, 2021.

[39] Z. Chen, L. Deng, G. Li*, J. Sun, X. Hu, L. Liang, Y. Xie, Effective and Efficient Batch Normalization Using Few Uncorrelated Data for Statistics' Estimation, IEEE Transactions on Neural Networks and Learning Systems, Vol. 32(1), pp. 348-362, 2021.

[40] J. Wu, Y. Chua, M. Zhang, G. Li, H. Li*, K. C. Tan, A tandem learning rule for effective training and rapid inference of deep spiking neural networks, IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2021.3095724, Accepted, 2021. 

[41] J. Ding, C. Wen, G. Li* et al, Target Controllability in Multilayer Networks via Minimum-cost Maximum-flow Method, IEEE Transactions on Neural Networks and Learning Systems, Vol. 32(5), pp. 1949-1962, 2021.

[42] F. Guo, G. Li*, C. Wen, L. Wang, Z. Meng, An Accelerated Distributed Gradient-Based Algorithm for Constrained Optimization With Application to Economic Dispatch in a Large-Scale Power System, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 51 (4), pp. 2041-2053, 2021.

[43] K. Song, X. Chen, P. Tang, G. Li*, L. Deng and J. Pei, Target Controllability of Two-layer Multiplex Networks based on Network Flow Theory, IEEE Transactions on Cybernetics, Vol. 51(5), pp. 2699-2711, 2021.

[44] Z. Qu, L. Deng, B. Wang, H. Chen, J. Lin, L. Liang, G. Li, Z. Zhang, Y. Xie, Hardware-Enabled Efficient Data Processing with Tensor-Train Decomposition, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Vol. 41(2), pp. 372-385, 2021

[45] J. Ding, C. Wen, G. Li* and Z. Chen, Key Nodes Selection in Controlling Complex Networks via Convex Optimization, IEEE Transactions on Cybernetics, vol. 51(1), pp. 52-63, 2021.

[46] L. Liang, J. Xu, L. Deng*, M. Yan, X. Hu, Z. Zhang, G. Li and Y. Xie, Fast Search of the Optimal Contraction Sequence in Tensor Networks, IEEE Journal of Selected Topics in Signal Processing, Vol. 15 (3), pp. 574-586, 2021.

[47] X. Liu, M Yan, L Deng, G. Li, X. Ye, D. Fan, Sampling methods for efficient training of graph convolutional networks: A survey, IEEE/CAA Journal of Automatica Sinica, vol.9 (2), pp. 205-234, 2021.

[48] Y. Lin, W. Ding, S. Qiang, L. Deng, G. Li*, ES-ImageNet: A Million Event-Stream Classification Dataset for Spiking Neural Networks, Frontiers in Neuroscience, 1546, 2021.

[49] Y. Tian, G. Li*, P. Sun*, Bridging the information and dynamics attributes of neural activities, Physical Review Research , vol. 3 (4), 043085, 2021

[50] Y. Guo, X. Zou, Y. Hu, Y. Yang, X. Wang, Y. He, R. Kong, Y. Guo, G. Li, W. Zhang, S. Wu, H. Li, A Marr's Three‐Level Analytical Framework for Neuromorphic Electronic Systems, Advanced Intelligent Systems, vol.3 (11), 2100054, 2021.

[51] D. Lee, D. Wang, Y. Yang, L. Deng, G. Zhao and G. Li*, QTTNet: Quantized tensor train neural networks for 3D object and video recognition, Neural Networks, Vol. 141, pp. 420-432, 2021. 

[52] F. Liu, M. Xu, G. Li, J. Pei, L. Shi, R. Zhao, Adversarial symmetric GANs: Bridging adversarial samples and adversarial networks, Neural Networks, Vol. 133, pp. 148-156, 2021.

[53] H. Chen, L. Deng, Q. Zheng, L. Liang, Y. Xie and G. Li*, Tensor Train Decomposition for Solving Large-Scale Linear Equations, Neurocomputing, Vol. 464, pp. 203-217, 2021.

[54] J. Yang, L. Deng, Y. Yang, Y. Xie and G. Li*, Training and inference for integer-based semantic segmentation network, Neurocomputing, Vol. 454, pp. 101-112, 2021. 

[55] C Ma, Q Zhao, G. Li, L Deng, G Wang, A deadlock-free physical mapping method on the many-core neural network chip, Neurocomputing , vol.401, pp. 327-337, 2021

[56] 胡一凡， 李国齐，吴郁杰，邓磊， 脉冲神经网络研究进展综述， 控制与决策，vol.36， pp.1-26， 2021.

[57] Y. Zhang, P. Qu, Y. Ji, W. Zhang, G. Gao, G. Wang, S. Song, G. Li, W. Chen, W. Zheng, F. Chen, J. Pei, R. Zhao, M. Zhao and L. Shi, A system hierarchy for brain-inspired computing, Nature, vol. 586 (7829), pp. 378-384, 2020.

[58] L. Deng, G. Li*, H. Song, Y. Xie and L. Shi, Model Compression and Hardware Acceleration for Neural Networks: A Comprehensive Survey, Proceedings of the IEEE, vol. 108 (4), pp. 485-532, 2020.

[59] L．Deng, G. Wang, G. Li et al, Tianjic: A Unified and Scalable Chip Bridging Spike-Based and Continuous Neural Computation, IEEE Journal of Solid-State Circuits, vol. 55(8), pp. 2228-2246, 2020.

[60] L. Deng, L. Liang, G. Wang, L. Chang, X. Hu, L. Liu, J. Pei, G. Li* and Y.Xie, SemiMap: A Semi-folded Convolution Mapping for Speed-Overhead Balance on Crossbars, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, vol. 39, pp.117-130, 2020.

[61] J. Ding, G. Li*, C. Wen, X. Yang, T. Hu, Sparsity-inspired optimal topology control of complex networks, IEEE Transactions on Network Science and Engineering, vol. 7 (3), pp. 1825-1839, 2020.

[62] G. Li*, X. Chen, P. Tang, G. Xiao, C. Wen and L. Shi, Target Control of Directed Networks based on Network Flow Problems, IEEE Transactions on Control of Network Systems, vol. 7(2), pp. 673-685, 2020.

[63] L. Gao, G. Zhao, G. Li*, F. Guo, F. Zeng, Optimal target control of complex networks with selectable inputs, IEEE Transactions on Control of Network Systems , vol.8 (1), pp.212-221, 2020

[64] Y. Zhou, G. Li*, H. Li, Automatic Cataract Classification Using Deep Neural Network with Discrete State Transition, IEEE Transactions on Medical Imaging, vol. 39(2), pp. 436-446, 2020.

[65] J. Huang, W. Wang, C. Wen and G. Li, Adaptive Event-Triggered Control of Nonlinear Systems with Controller and Parameter Estimator Triggering, IEEE Transactions on Automatic Control, vol. 65(1), pp. 318-324, 2020.

[66] G. Li*, F. Zeng, H. Li and A. K. Qin, Matrix Function Optimization Problems under Orthonormal Constraint, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.50, pp.802-814, 2020. 

[67] J Huang, W Wang, C Wen, J Zhou, G. Li, Distributed adaptive leader–follower and leaderless consensus control of a class of strict-feedback nonlinear systems: A unified approach, Automatica， vol.118, 109021, 2020

[68] Y Yang, J Huang, X Su, K Wang, G. Li, Adaptive control of second-order nonlinear systems with injection and deception attacks, IEEE Transactions on Systems, Man, and Cybernetics: Systems , vol. 52 (1), pp. 574-581,2020.

[69] D. Wang, G. Zhao, G. Li*, L. Deng and Y. Wu, Compressing 3DCNNs based on tensor train decomposition, Neural Networks, vol. 131, pp. 215-230, 2020.

[70] W. He, Y.J. Wu, L Deng, G. Li, H. Wang, Y. Tian, W. Ding, W. Wang, Y. Xie, Comparing snns and rnns on neuromorphic vision datasets: similarities and differences, Neural Networks, vol. 132, pp. 108-120, 2020.

[71] L. Deng, Y. Wu, X. Hu, L. Liang, Y. Ding, G. Li*, G. Zhao, P. Li, Y. Xie, Rethinking the performance comparison between SNNS and ANNS, Neural Networks, vol. 121, pp. 294-307, 2020.

[72] Y. Yang, L. Deng, S. Wu, T. Yan, Y. Xie and G. Li*, Training high-performance and large-scale deep neural networks with full 8-bit integers, Neural Networks, vol. 125, pp. 70-82, 2020. 

[73] B. Wu, D. Wang, G. Zhao, L. Deng and G Li*, Hybrid tensor decomposition in neural network compression, Neural Networks, vol. 132, pp. 309-320,2020.

[74] V. X. Nguyen, G. Xiao, J. Zhou, G. Li and B. Li, Bias in social interactions and emergence of extremism in complex social networks, Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30 (10), pp. 103110:1-10, 2020.

[75] N. Wu, L. Deng, G. Li and Y. Xie, Core Placement Optimization for Multi-chip Many-core Neural Network Systems with Reinforcement Learning, ACM Transactions on Design Automation of Electronic Systems (TODAES) , vol. 26, pp. 11:1-27, 2020. (Tier 4)

[76] J. Yan, F. Guo, C. Wen, G. Li, Parallel alternating direction method of multipliers, Information Sciences, vol. 507, pp. 185-196, 2020.

[77] M. Zhang, J. Wu, A. Belatreche, Z. Pan, X. Xie, Y. Chua, G. Li, H. Qu and H. Li, Supervised learning in spiking neural networks with synaptic delay-weight plasticity, Neurocomputing, vol. 131, 409, pp. 103-118, 2020.

[78] G. Li*, P. Tang, X. Chen, G. Xiao, M. Meng, C. Ma and L. Shi, Target Control and Expandable Target Control of Complex Networks, Journal of the Franklin Institute, vol. 357(6), pp. 3541-3564, 2020.

[79] M Meng, G Xiao, C Zhai, G Li, Z Wang, Distributed consensus of heterogeneous multi-agent systems subject to switching topologies and delays, Journal of the Franklin Institute, vol. 357 (11), pp. 6899-6917,2020

[80] T. Li, G. Li*, T. Xue, J. Zhang, Analyzing Brain Connectivity in the Mutual Regulation of Emotion–Movement Using Bidirectional Granger Causality, Frontiers in Neuroscience 14, 369, 2020

[81] G. Li*, L. Deng, Y. Chua, P. Li, E. O. Neftci and H. Li, Spiking neural network learning, benchmarking, programming and executing, Frontiers in Neuroscience, 14, 276, 2020

[82] J. Pei, L. Deng, S. Song, M. Zhao, Y. Zhang, S. Wu, G. Wang, Z. Zou, Z. Wu, W. He, F. Chen, S. Wu, Y. Wang, Y. Wu, Z. Yang, C. Ma, G. Li, W. Han, H. Li, H. Wu, R. Zhao, Y. Xie, and L. Shi, Towards Artificial General Intelligence with the Hybrid Tianjic Chip Architecture, Nature, vol. 572 (7767), pp. 106-111, 2019.

[83] S. Wu, G. Li (co-first author), L. Deng, L. Liu, Y. Xie and L Shi*, L1-Norm Batch Normalization for Efficient Training of Deep Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, vol.30(7), pp. 2043-2051, 2019.

[84] Y. Hao, T. Wang, G. Li* and C. Wen, Linear Quadratic Optimal Control of Time-invariant Linear Networks with Selectable Input Matrix, IEEE Transactions on Cybernetics, vol. 51(9), pp. 4743-4754., 2019.

[85] G. Li*, J. Ding, C. Wen and J. Huang, Minimum cost Control of Directed Networks with Selectable Control Inputs, IEEE Transactions on Cybernetics, vol. 49(12), pp. 4431-4440, 2019.

[86] G. Li*, J. Ding, C. Wen, L. Wang and F. Guo, Controlling Directed Networks with Evolving Topologies, IEEE Transactions on Control of Network Systems, vol.6, pp. 176-190, 2019.

[87] M. Meng, G. Xiao, C. Zhai and G. Li, Controllability of Markovian jump Boolean control Networks, Automatica, vol.106, pp. 70-76, 2019.

[88] H. Li, G. Li and L. Shi, Super-resolution of spatiotemporal event-stream image, Neurocomputing, vol. 335, pp.206-214, 2019.

[89] Q. Wan, F. Zeng, J. Yin, Y. Sun, Y. Hu, J. Liu, Y. Wang, G. Li and D. Guo, Phase-change Nano-clusters Embedded Memristor for Simulating Synaptic Learning, Nanoscale, vol. 11 (12), pp. 5684-5692, 2019.

[90] J. Yin, F. Zeng, Q. Wan, Y. Sun, Y. Hu, J. Liu, G. Li, F. Pan, Self-Modulating Interfacial Cation Migration Induced Threshold Switching in Bilayer Oxide Memristive Device, The Journal of Physical Chemistry C, vol. 123 (1), pp. 878-885,2019.

[91] Y Zhou, Q Fang, K Zhao, D Tang, H Zhou, G Li, X Xiang, T Hu, Robust task-oriented markerless extrinsic calibration for robotic pick-and-place scenarios, IEEE Access 7, pp. 127932-127942,2019

[92] M.T. Sadiq, X. Yu, Z. Yuan, F. Zeming, A.U. Rehman, I. Ullah, G. Li, G. Xiao, Motor Imagery EEG Signals Decoding by Multivariate Empirical Wavelet Transform-Based Framework for Robust Brain–Computer Interfaces, IEEE Access, vol. 7, pp. 171431-171451, 2019.

[93] M.T. Sadiq, X. Yu, Z. Yuan, Z. Fan, A.U. Rehman, G. Li and G. Xiao, Motor imagery EEG signals classification based on mode amplitude and frequency components using empirical wavelet transform, IEEE Access, vol. 7, pp. 127678-127692, 2019.

[94] J. Xu, L. Liang, L. Deng, C. Wen, Y. Xie and G. Li*, Towards a polynomial algorithm for optimal contraction sequence of tensor networks from trees, Physical Review E, vol. 100, pp. 043309:1-16, 2019.

[95] L. Gao, G. Zhao, G. Li*, J. Huang, Y. Liu and L. Deng, Containment Control of Directed Networks with Time-varying Nonlinear Multi-agents using Minimum Number of Leaders, Physica A: Statistical Mechanics and its Applications, vol.526, pp.120859:1-12, 2019.

[96] L. Gao, G. Zhao, G. Li*, Y. Liu, J. Huang and C. Wen, Allocating Minimum Number of Leaders for Seeking Consensus over Directed Networks with Time-varying Nonlinear Multi-agents, International Journal of Control, Automation and Systems，vol. 17(1), pp. 57-68, 2019. 

[97] J. Huang, Y. Song, W. Wang*, C. Wen and G. Li, Fully Distributed Adaptive Consensus Control of a Class of High-order Nonlinear Systems with a Directed Topology and Unknown Control Directions, IEEE Transactions on Cybernetics, vol.48, pp. 2349- 2356, 2018.

[98] Z. Meng*, T. Yao, G. Li, W. Ren and D. Wu, Synchronization of coupled dynamical systems: tolerance to weak connectivity and arbitrarily bounded time-varying delays, IEEE Transactions on Automatic Control, vol.63, pp.1791-1797, 2018.

[99] L. Deng, P. Jiao, J. Pei, Z. Wu and G Li*, GXNOR-Net: Training deep neural networks with ternary weights and activations without full-precision memory under a unified discretization framework, Neural Networks, vol.100, pp.49-58, 2018. 

[100] Z. Zhang, T. Li, Y. Wu, Y. Jia, C. Tan, X. Xu, G. Wang, J. Lv, W. Zhang, Y. He, J. Pei, C. Ma, G. Li, H. Xu, L. Shi, H. Peng and H. Li, Truly Concomitant and Independently Expressed Short‐ and Long‐Term Plasticity in a Bi2O2Se‐Based Three‐Terminal Memristor, Advanced Materials, vol. 31, pp. 1805769:1-10, 2018.

[101] J. Yin, F. Zeng*, Q. Wan, F. Li, Y. Sun, Y. Hu, J. Liu, G. Li, F. Pan, Adaptive Crystallite Kinetics in Homogenous Bilayer Oxide Memristor for Emulating Diverse Synaptic Plasticity, Advanced Functional Materials, 1706927, 2018. 

[102] Y. Zhang, W. He, Y. Wu, K. Huang, Y. Shen, J. Su, Y. Wang, Z. Zhang, X Ji, G. Li, H. Zhang, S. Song, H. Li, L. Sun, R. Zhao and L. Shi, Highly Compact Artificial Memristive Neuron with Low Energy Consumption, Small, vol. 14(51), pp. 1802188:1-8, 2018. 

[103] H. Li, G. Li and L. Shi*, Deep Representation via Convolutional Neural Network for Classification of Spatiotemporal Event Streams, Neurocomputing, vol.299, pp.1-9, 2018.

[104] G. Li*, L. Deng, L. Tian, H. Cui, W. Han, J. Pei and L. Shi, Training Deep Neural Networks with Discrete State Transition, Neurocomputing, vol.272, pp.154-162, 2018. 

[105] L. Liang, L. Deng, Y. Zeng, X. Hu, Y. Ji, X. Ma, G. Li* and Y. Xie*， Crossbar-aware neural network pruning, IEEE Access, vol. 6, pp. 58324-58337, 2018.

[106] J. Xu, G. Li*, C. Wen, K. Wu and L. Deng, Towards a Unified Framework of Matrix Derivatives, IEEE Access, vol.6, pp. 47922-47934 , 2018.

[107] L. Gao, S. Zhao, G. Li*, L. Deng and F. Zeng, Towards the Minimum-cost Control of Target Nodes in Directed Networks with Linear Dynamics, Journal of the Franklin Institute, vol. 355 (16), pp. 8141-8157, 2018.

[108] G. Li*, P. Tang, G. Xiao, C. Wen, J. Pei and L. Shi, Optimization on Matrix Manifold based on Gradient Information and Its Applications in Network Control, Physica A: Statistical Mechanics and its Applications, vol.508, pp.481-500, 2018.

[109] W. Yang, A. Zhang, H. Zhang, J. Wang and G. Li, Adaptive integral sliding mode direct power control for VSC‐MVDC system converter stations, International Transactions on Electrical Energy Systems , vol.28 (4), e2516, 2018

[110] V.X. Nguyen, G. Xiao*, X.J. Xu, G. Li and Z. Wang, Opinion Formation on Multiplex Scale-free Networks, Europhysics Letters, 121, 26002, 2018. 

[111] P. Tang, G. Li*, G. Xiao, R. Wang, C. Ma and L. Shi, Matrix Function Optimization under Weighted Boundary Constraints and Its Applications in Network Control, ISA Transactions, vol.80, pp. 232-243, 2018. 

[112] L. Deng, G. Li*, J. Pei and J. Huang, “L0 Norm Constraint based external control source allocation for the minimum cost control of directed networks”, ISA Transactions，vol.76, pp. 88-96, 2018.

[113] G. Li*, P. Tang, C. Wen, C. Ma and C. Wen, Key-nodes Selection Problem for Minimum Cost Control of Directed Networks, Optimal Control and Applications and Methods, vol. 39 (1), pp. 95-113, 2018.

[114] L. Deng, Z. Zou, X. Ma, L. Liang, G. Wang, X. Hu, L. Liu, J. Pei, G. Li* and Y. Xie*, Fast Object Tracking on a Many-core Neural Network Chip, Frontiers in Neuroscience, vol. 12, pp. 841:1-15, 2018.

[115] Y. Wu, L. Deng, G. Li, J. Zhu, L. Shi*, Spatio-temporal backpropagation for training high-performance spiking neural networks, Frontiers in Neuroscience, 12, 331, 2018. 

[116] G. Li*,　L. Deng, P. Tang, G. Xiao, C. Wen, J. Pei, W. Hu, L. Shi and H. Eugene Stanley, Enabling Controlling Complex Networks with Local Topological Information, Scientific Reports, vol. 8(1), 4593, 2018.

[117] 王鼎衡， 赵广社， 李国齐， 邓磊, 基于张量链压缩的卷积神经网络及手势识别应用研究国自动化大会 (CAC2018) 论文集, 2018

[118] G. Li*, P. Tang*, C. Wen and Z. Meng, Boundary Constraints for Minimum Cost Control of Directed Networks, IEEE Transactions on Cybernetics, vol.47, pp.4196-4209, 2017. 

[119] J. Huang, Y. Song, W. Wang, C. Wen and G. Li, Smooth Control Design for Adaptive Leader-following Consensus Control of a Class of High-order Nonlinear Systems with Time-varying Reference, Automatica, vol.83, pp.361-367, 2017. 

[120] Y. Yu, G. Xiao*, G. Li, and W. P. Tay, Opinion diversity and community formation in adaptive networks, Chaos, vol. 27 (10), 103115, 2017. 

[121] L.Gao, G. Zhao, G. Li* and Z. Yang, Leader Selection Problem for Stochastically Forced Consensus Networks based on Matrix Differentiation, Physica A: Statistical Mechanics and its Applications, vol.469, pp. 799–812, 2017. 

[122] J. Ding, C. Wen and G. Li*, Key node selection in minimum-cost control of complex networks, Physica A: Statistical Mechanics and its Applications, vol.486, pp.251-261, 2017. 

[123] Z. Yang, G. Zhao, G. Li* and H. Rong, Matrix Differentiation for Gaussian Multiple Access Channels under Weighted Total Power Constraint, Annals of Telecommunications, vol.72, pp.703-715, 2017. (Tier 4)

[124] W. Yang, A. Zhang, J. Li, G. Li, H. Zhang* and J. Wang, Integral plus Resonant Sliding Mode Direct Power Control for VSC-HVDC System under Unbalanced Grid Voltage Conditions, Energies, vol. 10, 1528, 2017.  

[125] H. Li, H. Liu, X. Ji, G. Li and L. Shi*, CIFAR10-DVS: An Event-stream Dataset for Object Classification, Frontiers in Neuroscience, vol.11, 309, 2017.

[126] Y. Hu, F. Zeng, C. Chang, W. Dong, X. Li, F. Pan, G. Li , Diverse Synaptic Plasticity Induced by the Interplay of Ionic Polarization and Doping at Salt-Doped Electrolyte/Semiconducting Polymer Interface, ACS Omega , vol. 2(2), pp.746-754, 2017.

[127] C. T. Chang, F. Zeng*, J. Li, W. Dong, Y. Hu and G. Li, Spatial summation of the short-term plasticity of a pair of organic heterogeneous junctions, RSC Advances, vol. 7, pp.4017-4023, 2017. 

[128] F. Guo, C. Wen, J. Mao, Y. Song and G. Li*, A Distributed Hierarchical Algorithm for Multi-Cluster Constrained Optimization, Automatica, vol. 77, pp. 230-238, 2016.

[129] J. Ding, C. Wen, G. Li* and C. S. Chua, Locality sensitive batch feature extraction for high-dimensional data, Neurocomputing, vol.171, pp. 664-672, 2016. 

[130] G. Li*, J. Ding, C. Wen and J. Pei, Optimal control of complex networks based on matrix differentiation, Europhysics Letters, vol. 115, 68005, 2016.

[131] G. Li*, W. Hu, G. Xiao, L. Deng, P. Tang, J. Pei and L. Shi, Minimum Cost Control of Complex Networks, New Journal of Physics, vol. 18 (1), 013012, 2016.

[132] H. Y. Xu, S. H.Kuo*, G. Li, E. F. T. Legara, D. Zhao, and C. P. Monterola*, Generalized Cross Entropy Method for estimating joint distribution from incomplete information. Physica A: Statistical Mechanics and its Applications, vol.453, pp.162-172, 2016. 

[133] L. Deng, W. Dong, Z. Y. Zhang, P. Tang, G. Li* and J. Pei, Energy consumption analysis for various memristive networks under different learning strategies, Physics Letters A, vol.380, pp.903–909, 2016. 

[134] G. Li* , L. Deng, D. Wang, F. Zeng, W. Wang, Z. Zhang, H. Li, J. Pei , L. Shi and S. Song, Hierarchical Chunking of Sequential Memory on Neuromorphic Architecture with Reduced Synaptic Plasticity, Frontiers in Computational Neuroscience, vol. 10, 136, 2016.

[135] W. Dong, F. Zeng*, Y. Hu, C. Chang, X. Li and F. Pan and G. Li, Sliding threshold of spike-rate dependent plasticity of a semiconducting polymer/electrolyte cell, Journal of Polymer Science, Part B: Polymer Physics, vol. 54(23): 2412-2417. 2016. (Tier 4)

[136] G. Li, C. Wen*, A. Zhang, Fixed Point Iteration in Identifying Bilinear Models, Systems and Control Letters, vol. 83, pp. 28-37, 2015.

[137] G. Li, C. Wen*, W.X. Zheng, and G. Zhao, Iterative Identification of Block-Oriented Systems based on Biconvex Optimization, Systems and Control Letters, vol.79, pp.68-75, 2015. 

[138] G. Li*, D. Zhao, Y. Xu, S.H. Kuo, H.Y. Xu, N. Hu, G. Zhao and C. Monterola, “Entropy Based Modelling for Estimating Demographic Trends”, PloS one, vol.10 (9), e0137324, 2015. 

[139] G. Li*, K. Ramanathan, N. Ning, L. Shi and C. Wen, Memory Dynamics in Attractor Networks, Computational Intelligence and Neuroscience, vol. 2015, 191745, 2015.

[140] G. Li*, L. Deng, Y. Xu, C. Wen, W. Wang, J. Pei, and L. Shi, Temperature based Restricted Boltzmann Machines, Scientific Reports, vol. 6, 19133, 2015. 

[141] L. Deng, G. Li* (co-first author), N. Deng, D. Wang, Z. Zhang, W. He, H. Li, J. Pei and L Shi, Complex Learning in Bio-plausible Memristive Networks, Scientific Reports, vol. 5,10684, 2015.

[142] G. Li*, N. Ning* (co-first author), L. Pan, K. Ramanathan, R. Zhao and L. Shi, A Simple Model of Persistent Firing Neurons, International Journal of Intelligence Science, vol. 5, pp. 89-101, 2015.

[143] G. Li*, G. Zhao, and F. Yang, Online Learning with Kernels in Classification and Regression, Evolving Systems, vol. 5, pp.11-19, 2014. (Tier 3)

[144] H. Wei, K. Huang, N. Ning, G. Li, K. Ramanathan*, R. Zhao, Y. Jiang and L. Shi. Enabling an Integrated Rate-temporal Learning Scheme on Memristor, Scientific Reports, vol.4, 4755, 2014.

[145] G. Li, C. Wen*, W. Wei, Y. Xu, J. Ding, G. Zhao and L. Shi, Trace ratio criterion for feature extraction in classification, Mathematical Problems in Engineering, vol. 2014, 725204, 2014.  

[146] L. Zhang，A. Zhang*, Z. Ren, G. Li, C. Zhang, and J. Han, Hybrid adaptive robust control of static var compensator in power systems, International Journal of Robust and Nonlinear Control, vol.24, pp. 1707-1723, 2014. 

[147] G. Li, C. Wen*, Z. G. Li, A. Zhang, Y. Feng, and K. Z. Mao, Model Based Online Learning with Kernels, IEEE Transactions on Neural Networks and Learning systems, vol.24, pp. 356-369, 2013. 

[148] G. Li and C. Wen*, Convergence of fixed-point iteration for the identification of Hammerstein and Wiener systems, International Journal of Robust and Nonlinear Control, vol. 23, pp. 1510-1523, 2013. 

[149] G. Li*, N. Ning, K. Ramanathan, H. Wei, L. Pan, and Liu Shi, Behind the Magical Numbers: Hierarchical Chunking and the Human Working Memory Capacity, International Journal of Neural Systems, vol. 23, 1350019, 2013. 

[150] G. Li and C. Wen*, Identification of Wiener Systems with Clipped Observations, IEEE Transactions on Signal Processing, vol. 60, pp. 3845-3852, 2012. 

[151] K. Ramanathan*, N. Ning, D. Dhanasekar, G. Li, L. Shi and P. Vadakkepat, Presynaptic Learning and Memory with A Persistent Firing Neuron and A Habituating Synapse: Model of Short Term Persistent Habituation, International Journal of Neural Systems，vol. 22, 1250015, 2012. 

[152] G. Li, C. Wen*, W. X. Zheng, and Y. Chen, Identification of a Class of Nonlinear Autoregressive Models with Exogenous Inputs based on Kernel Machines, IEEE Transactions on Signal Processing, vol.59, pp.2146-2159, 2011. 

[153] G. Li, C. Wen*, G. B. Huang, and Y. Chen: Error tolerance-based support vector machine for regression, Neurocomputing, vol. 74, pp. 771-782, 2011. 

[154] G. Li and C. Wen*, Convergence of Normalized Iterative Identification of Hammerstein Systems, Systems and Control Letters, vol. 60, pp. 929-935, 2011. 

[155] Y. Chen, C. Wen*, G. Tao, M. Bi, and G. Li, Continuous and Noninvasive Blood Pressure Measurement: A Novel Modeling Methodology of the Relationship between Blood Pressure and Pulse Wave Velocity, Annals of Biomedical Engineering, vol. 37, pp. 2222-2233, 2009. 

[156] 李国齐, 赵广社, 孙照莹, 基于样本相似度曲面重构的核函数构造, 信息与控制, vol. 36, pp. 47-55, 2007.

[157] 李国齐, 赵广社, 孙照莹, Fisher准则K-L变换和SVM在分类中的应用, 计算机工程与应用, vol. 42, pp. 147-151, 2006.

Conference  Papers

[158] Q. Su, Y. Chou, Y. Hu, J. Li, S. Mei, Z. Zhang, G. Li*，Deep Directly-Trained Spiking Neural Networks for Object Detection, IEEE International Conference on Computer Vision (ICCV 2023), 2023.

[159] M. Yao, J. Hu, G. Zhao, Y. Wang, Z. Zhang, B. Xu and G, Li*, Inherent Redundancy in Spiking Neural Networks

 IEEE International Conference on Computer Vision (ICCV 2023), 2023.

[160] Y. Du, F. Wei, Z. Zhang, M. Shi, Y. Gao and G. Li, Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

[161] L. Liang, Z. Chen, L. Deng, F. Tu, G. Li, Y. Xie, Accelerating spatiotemporal supervised training of large-scale spiking neural networks on GPU, Design, Automation & Test in Europe Conference & Exhibition (DATE), 658-663,2022.

[162] X. Liu, M. Yan, L. Deng, G. Li, X. Ye, D. Fan, S. Pan and Y. Xie, Survey on Graph Neural Network Acceleration: An Algorithmic Perspective, 30th International Joint Conference on Artificial Intelligence (IJCAI-22), 2022.

[163] M. Yao, H. Gao, G Zhao, D. Wang, Y. Lin, Z. Yang and G Li*, Temporal-wise Attention Spiking Neural Networks for Event Streams Classification, IEEE International Conference on Computer Vision (ICCV 2021), pp. 10221-10230, 2021.

[164] R. Huang, Y. Ma, T. Wang, G. Li, M. Yan, A K-Nearest Centroid Neighbor with attention Classifier, 5th International Conference on Computer Science and Artificial Intelligence, 156-161,2021.

[165] D. Gao, Z. Wu, Y. Wu, G. Li, J. Pei, ARLIF: A Flexible and Efficient Recurrent Neuronal Model for Sequential Tasks, International Workshop on Human Brain and Artificial Intelligence, 1-13, 2021.

[166] H. Zheng, Y. Wu, L. Deng, Y. Hu and G. Li*, Going deeper with directly-trained larger spiking neural networks, Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-21), pp. 11062-11070, 2021.

[167] M. Xu, Y. Wu, L.Deng, F. Liu, G Li, J Pei, Exploiting Spiking Dynamics with Spatial-temporal Feature Normalization in Graph Learning, 30th International Joint Conference on Artificial Intelligence (IJCAI-21), Montreal-themed virtual reality, 2021.

[168] Z Chen, M Yan, M Zhu, L Deng, G Li, S Li, Y Xie, fuseGNN: accelerating graph convolutional neural network training on GPGPU, 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 1-9, 2020.

[169] Y. Wu, L. Deng, G. Li, J. Zhu and L. Shi, Direct Training Spiking Neural Networks: Faster, Larger and Better, 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), Honolulu, HI, United states, pp. 1311-1318, 2019.

[170] Y. Yang, L. Deng, P. Jiao, Y. Chua, J. Pei, C. Ma and G Li*, Transfer learning in general lensless imaging through scattering media, Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications (ICIEA 2020), Virtual, Kristiansand, Norway, pp. 1132-1141, 2020.

[171] Y. Yang, F. Wei, M .Shi, G. Li*, Restoring negative information in few-shot object detection, 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Virtual, Online, 2020.

[172] Q. Zhao, L. Deng, G. Li, G. Wang and C. Ma, Efficient Mapping without Deadlock on the Many-core Neural Network Chip, IEEE 15th International Conference on Control and Automation (ICCA), 2019.

[173] J Wu, Y Chua, M Zhang, Q Yang, G Li, H Li, Deep spiking neural network with spike count based learning rule,2019 International Joint Conference on Neural Networks (IJCNN), 1-6, 2019.

[174] J. Wang, G. Zhao, D. Wang, G. Li, Tensor Completion using Low-Rank Tensor Train Decomposition by Riemannian optimization, 2019 Chinese Automation Congress (CAC), Hangzhou, China, pp. 3380-3384, 2019.

[175] L. Liu, L. Deng, X. Hu, M. Zhu, G. Li, Y. Ding and Y Xie, Dynamic Sparse Graph for Efficient Deep Learning, 7th International Conference on Learning Representations (ICLR 2019), New Orleans, LA, United states, 2019.

[176] P. Wang, X. Xie, L. Deng, G. Li, D. Wang and Y. Xie, HiNet: Hybrid Ternary Recurrent Neural Networks, 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 2018.

[177] S. Wu, G. Li, L. Shi and C. Feng, Training and Inference with Integers in Deep Neural Networks, International Conference on Learning Representations(ICLR), Vancouver, BC, Canada, 2018.

[178] G. Zhao, L. Gao,G. Li, Y. Liu, J. Huang and C. Wen, Leader-Follower Consensus over Directed Networks with Time-varying Nonlinear Multi-agents using Minimum Number of Leaders, 14th International Conference on Control and Automation (ICCA), Anchorage, AK, USA, pp. 624-629, 2018.

[179] J. Ding, C. Wen and G. Li*. Optimal control of weighted networks based on node connection strength, 2017 IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, UK, pp. 520-525, 2017.

[180] R.Wang, G.Xiao, P.Wang, Y.Cao, G.Li, J. Hao and K. Hu, Energy Generation Scheduling in Microgrids Involving Temporal-Correlated Renewable Energy. 2017 IEEE Global Communications Conference (GLOBECOM 2017), Singapore, pp. 1-6, 2017.

[181] L. Shi, J. Pei, N. Dong and G. Li et al. Development of a neuromorphic computing system, IEEE International Electron Devices Meeting (IEDM), Washington, DC, USA, 2015.

[182] G. Li, J. Pei, C. Wen, Z. Li, G. Zhao and L. Shi, Hierarchical Encoding of Human Working Memory, 10th IEEE Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand, pp. 866-871, 2015.

[183] G. Zhao, Y. Xu, G. Li and Z. Yang, An Entropy based Method for Estimating Demographic Trends, IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Douai, France, 2015.

[184] H. M. Li, J. Pei and G. Li, Real-time tracking based on neuromorphic vision, 15th IEEE Non-Volatile Memory Technology Symposium (NVMTS 2015), Beijing, China, 2015.

[185] L. Deng, D. Wang, G. Li, Z. Zhang and J. Pei, A new computing rule for neuromorphic engineering, 15th IEEE Non-Volatile Memory Technology Symposium (NVMTS 2015), Beijing, China, 2015.

[186] F. Guo, C. Wen, G. Li and J. Chen, Distributed Economic Dispatch for a Multi-Area Power System, 10th IEEE Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand, pp. 620-625, 2015.

[187] Z. Yang, G. Zhao, H. Rong and G. Li, Nonparametric Robust Adaptive Controller for Tracking AMB Systems, 10th IEEE Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand, pp. 113-118, 2015.

[188] J. Ding, G. Li, C. Wen and C. Chua, Min-Max Discriminant Analysis Based On Gradient Method For Feature Extraction, 13th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, pp. 129-134, 2014.

[189] L. Meng, C. Wen and G. Li, Support Vector Machine based Liver Cancer Early Detection using Magnetic Resonance Images, 13th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, pp. 861-864, 2014.

[190] G. Li,* N. Ning, K. Ramanathan, and L. Shi, Revised Online Learning with Kernels, IEEE Symposium Series on Computational Intelligence (SSCI), Singapore, pp. 275-279, 2013.

[191] G. Li*, C. Wen, W. X. Zheng, and G. Zhao, Iterative method in the identification of block-oriented systems based on biconvex optimization, 16th IFAC Symposium on System Identification, SYSID 2012, Bruxelles, Belgium, pp.31-36, 2012.

[192] G. Li*, C. Wen, D. Cui, and F. Yang, A New Method of Online Learning with Kernels for Regression, 7th IEEE Conference on Industrial Electronics and Applications (ICIEA), Singapore, pp. 1291-1296, 2012.

[193] G. Li* and G. Zhao, Online Learning with Kernels in Classification and Regression, IEEE Conference on Evolving and Adaptive Intelligent Systems (IEEE EAIS), Madrid, Spain, pp.17-22, 2012.

[194] G. Li, C. Wen*, and Z. G. Li, A New Online Learning with Kernels Method in Novelty Detection, The 37th Annual Conference of the IEEE Industrial Electronics Society (IECON), Melbourne, Australia, pp. 2311-2316, 2011.

[195] G. Zhao, G. Li, C. Wen*, and F. Yang, On the convergence of iterative identification of Hammerstein systems, 2011 International Conference on Automation and Robotics ICAR2011, Dubai, United arab emirates, pp. 303-312, 2011.

[196] G. Li and C. Wen*, Legendre Polynomials in Signal Reconstruction and Compression, 5th IEEE Conference on Industrial Electronics and Applications (ICIEA), Taiwan, pp. 1636-1640, 2010.

[197] G. Li and C. Wen*, Identification of Wiener Systems based on Fixed Point Theory, 11th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, pp. 491-496, 2010.

lNational Science Fund for Outstanding Young Scholars,2023

lBest Paper Award, China Computing Power Conference,2023.

lChina Intelligent Computing Technology Innovation Award,2023

lYouth Pioneer of Chinese Computing Power Award,2023

lSecond Prize of Technology Invention Award of the Ministry of Education,2022

lHundred Talents Program of the Chinese Academy of Sciences，2022

lSecond Prize, Science and Technology Progress Award of Fujian Province，2022.

lBeijing Science Fund for Distinguished Young Scholars, 2021

lYoung Scientist Award of Beijing Academy of Artificial Intelligence, 2019

l"Brain inspired computing completeness concept and computing system hierarchy",  Top ten progress of global artificial intelligence in 2020 (selected by Beijing Academy of Artificial Intelligence), 2020. 

l"Building heterogeneous chips for artificial general intelligence", Top ten scientific advances Award in China selected by the Ministry of science and technology, P.R. China (as the backbone of the team member) 

l"Heterogeneous integration Tianji chip for artificial general intelligence" , the world advanced scientific and Technological Achievement Award of the 2019 World Internet Conference  (as the backbone of the team member).

lBeijing Natural Science Foundation Excellent Young Talent Award, 2018.

lFirst Class Prize in Science and Technology of Chinese Institute of Command and Control, 2018.

lBest Poster Paper Award, 15th IEEE Non-Volatile Memory Technology Symposium, Beijing, China, 2015.

lBest Paper Award, IEEE Conference on Evolving and Adaptive Intelligent Systems, Madrid, Spain, 2012.