Work Experience：

• 2020年至今为浙江大学计算机学院教授。
• 2014年-2020年担任四川大学类脑计算研究中心主任
• 2008-2015年于新加坡科技研究局资讯通信研究院担任认知计算和机器人认知实验室主任
• 2006–2008年于澳大利亚昆士兰大学脑科学研究所从事博士后研究
• 2004–2006年在意法半导体公司担任研发工程师

Tang Huajin is a Qiushi Distinguished Professor, Doctoral Supervisor and IEEE Fellow with Zhejiang University, and has been selected into the National High-Level Talent Program. He obtained his Bachelor’s, Master’s and Doctoral degrees from Zhejiang University, Shanghai Jiao Tong University and National University of Singapore respectively. He has long been engaged in research on brain-inspired computing, neuromorphic computing and chips, intelligent robots and other related fields. He has presided over multiple national-level projects, including key research and development projects of the Ministry of Science and Technology and key projects of the National Natural Science Foundation of China. His research achievements have won several Outstanding Paper Awards of SCI Q1 journals, such as the 2016 IEEE Transactions on Neural Networks and Learning Systems Outstanding Paper Award, the 2019 IEEE Computational Intelligence Magazine Outstanding Paper Award, and the 2023 Neural Networks Best Paper Award. For his contributions to neural computing and related fields, he was awarded the 2024 Outstanding Achievement Award of the Asia Pacific Neural Network Society (APNNS). He currently serves as the Editor-in-Chief of IEEE Transactions on Cognitive and Developmental Systems. Looking forward to collaboration in in the fields of artificial intelligence and brain-inspired computing, as well as PhD student  applications.

Representative Publications：

1.Ma G, Yan R, Tang H*. Exploiting noise as a resource for computation and learning in spiking neural networks.Patterns, 4, 100831,2023(cell子刊，DOI: 10.1016/j.patter.2023.100831 )

2.Gu P, Xiao R, Pan G, Tang H*. STCA: Spatio-temporal credit assignment with delayed feedback in deep spiking neural networks. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligenc, vol. 15, pp. 1366-1372. 2019.(CCF-A, DOI:10.24963/ijcai.2019/189 )

3.Dang S, Wu Y, Yan R, Tang H*. Why grid cells function as a metric for space. Neural Networks 142 (2021): 128-137.(SCI 2区, DOI: 10.1016/j.neunet.2021.04.031 )

4.Wang Z, Jiang R, Lian S, Yan R, Tang H*. Adaptive smoothing gradient learning for spiking neural networks.In International conference on machine learning, pp. 35798-35816. PMLR, 2023.CCF-A

5.Ma G, Jiang R, Wang L, Tang H*. Tang. Dual memory model for experience-once task-incremental lifelong learning. Neural Networks, Neural Networks 166 (2023): 174-187( SCI 2区, DOI: 10.1016/j.neunet.2023.07.009 )

6.Ma G, Jiang R, Yan R, Tang H*. Temporal conditioning spiking latent variable models of the neural response to natural visual scenes. Advances in Neural Information Processing Systems 36 (2023): 3819-3840.CCF-A

7.Lian S, Shen J, Liu Q, Wang Z, Yan R, Tang H*. Learnable Surrogate Gradient for Direct Training Spiking Neural Networks. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pp. 3002-3010. 2023.(CCF-A, DOI: 10.24963/ijcai.2023/335 )

8.Qin L, Yan R, Tang H*. A low latency adaptive coding spike framework for deep reinforcement learning.In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, pp. 3049-3057. 2023.(CCF-A, DOI:10.24963/ijcai.2023/340 )

9.Qin L, Wang Z, Yan R, Tang H*. Attention-based deep spiking neural networks for temporal credit assignment problems.IEEE Transactions on Neural Networks and Learning Systems.2023,35(8): 10301-10311.(SCI 1区, DOI:10.1109/tnnls.2023.3240176 )

10.Wang Z, Zhang Y, Lian S, Cui X, Yan R, Tang H*. Toward high-accuracy and low-latency spiking neural networks with two-stage optimization. IEEE Transactions on Neural Networks and Learning Systems.2023,36(2),3189-3203.(SCI 1区, DOI:10.1109/tnnls.2023.3337176 )

11.Shen J, Ni W, Xu Q, Tang H*. Efficient spiking neural networks with sparse selective activation for continual learning. In Proceedings of the AAAI Conference on Artificial Intelligence,2024,38(1): 611-619.(CCF-A, DOI:10.1609/aaai.v38i1.27817 )

12.Wang Z, Wang Z, Li H, Qin L, Jiang R, Ma D, Tang H*. Eas-snn: End-to-end adaptive sampling and representation for event-based detection with recurrent spiking neural networks. In European Conference on Computer Vision,2024,310-328.(CCF-B, DOI:10.1007/978-3-031-73027-6_18 )

13.Tang H*, Gu P, Wijekoon J, Alsakkal MA, Wang Z, Shen J, Yan R, Pan G. Neuromorphic auditory perception by neural spiketrum. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024,9(1),292-303. (SCI 2区, DOI:10.1109/tetci.2024.3419711 )

14.Zhang J, Shen J*, Wang Z, Guo Q, Yan R, Pan G, Tang H*. SpikingMiniLM: energy-efficient spiking transformer for natural language understanding. Science China Information Sciences, 2024,67(10): 200406.(CCF-A,DOI:10.1007/s11432-024-4101-6 )

15.Ma G, Wang H, Zhao J, Yan R, Tang H*. Successive POI recommendation via brain-inspired spatiotemporal aware representation.In Proceedings of the AAAI Conference on Artificial Intelligence, 2024,38(1): 574-582.(CCF-A, DOI:10.1609/aaai.v38i1.27813 )

16.Ni W, Shen J, Xu Q, Tang H*. ALADE-SNN: Adaptive Logit Alignment in Dynamically Expandable Spiking Neural Networks for Class Incremental Learning.In Proceedings of the AAAI Conference on Artificial Intelligence, 2025,39(18): 19712-19720.(CCF-A, DOI:10.1609/aaai.v39i18.34171)

Books/Monographs：

1.《Neuromorphic Cognitive Systems: A Learning and Memory Centered Approach》，Springer，2017年，参编

2.《类脑计算研究前沿》，上海交通大学出版社，2022年，参编

Patents：

1.一种面向动态视觉传感器的类脑手势序列识别方法，ZL202110237539.9，2021年，第一发明人

2.一种基于事件相机的脉冲神经网络目标跟踪方法和系统，ZL202210357273.6，2022年，第二发明人

3.一种基于稀疏编码的听觉脉冲编码方法及系统，ZL202010273268.8，2022年，第一发明人

4.一种基于FPGA的图像脉冲编码方法及系统，ZL202010385501.1，2023年，第一发明人

5.一种基于类脑时空感知表征的兴趣点推荐方法及系统，ZL202110930940.0，2023年，第一发明人

6.一种基于脉冲卷积神经网络的目标追踪方法及系统，ZL202210407708.3，2025年，第一发明人

7.一种类脑感知-学习-决策系统及方法，ZL202310757825.7，2025年，第一发明人

8.一种基于脉冲神经网络的自然语言处理方法及系统，ZL202410950860.5，2025年，第一发明人

9.一种秀丽隐杆线虫仿生多感知-运动行为方法及系统，ZL202310562274.9，2025年，第一发明人

Research Awards：

• 2016年：IEEE Transactions on Neural Networks and Learning Systems最佳论文奖，IEEE计算智能协会
• 2019年：IEEE Computational Intelligence Magazine最佳论文奖，IEEE计算智能协会
• 2023年：Neural Network最佳论文奖，国际神经网络学会
• 2024年：APNNS杰出成果奖，亚太神经网络学会