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ProfessorInstitute of Automation, Chinese Academy of Sciences, ChinaUniversity of Chinese Academy of Sciences, China |
Guoqi Li obtained his PhD from Nanyang Technological University, Singapore, in 2011. From 2011 to 2014, he worked as a Scientist at the Data Storage Institute and the Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore. From 2014 to 2022, he served as an Assistant Professor and later an Associate Professor at Tsinghua University, Beijing, China. Since 2022, he has been affiliated with the Institute of Automation, Chinese Academy of Sciences, and the University of Chinese Academy of Sciences, where he currently holds the position of Full Professor. His research focuses on Brain-inspired Intelligence, Neuromorphic Computing, and Spiking Neural Networks. He has authored or co-authored over 200 papers in prestigious journals such as Nature, Nature Communications, Science Robotics, Proceedings of the IEEE, as well as top AI conferences including ICLR, NeurIPS, ICML, AAAI, among others. His papers have been cited more than 8800 times according to Google Scholar.
Dr. Li hhas actively contributed to various professional services, including serving as a Tutorial Chair, an International Technical Program Committee Member, a PC member, a Publication Chair, a Track Chair, and a workshop chair for several international conferences. He holds positions as an Editorial-Board Member for Control and Decision, and Associate Editor for IEEE Transactions on Neural Networks and Learning Systems, Neuromorphic Computing and Engineering, Frontiers in Neuroscience: Neuromorphic Engineering, and Journal of Control and Decision. Additionally, he has served as a Guest Editor for IEEE Transactions on Cognitive and Developmental Systems and Frontiers in Computational Neuroscience. Dr. Li also acts as a reviewer for Mathematical Reviews published by the American Mathematical Society and serves as a PC member for several top AI conferences, including ICLR, NeurIPS, ICML, AAAI, among others. He was the recipient of the Second Class Prize for Technological Invention by the Chinese Ministry of Education in 2022, the First Class Prize in Science and Technology from the Chinese Institute of Command and Control in 2018, and the Top Ten Scientific Advances Award in China selected by the Ministry of Science and Technology, P.R. China, where he was recognized as a backbone team member. He also received the 2020 Second Prize of the Fujian Provincial Science and Technology Progress Award. Dr. Li was honored with the Outstanding Young Talent Award from the Beijing Natural Science Foundation in 2021, and was selected to participate in the Hundred Talents Program of the Chinese Academy of Sciences in 2022. In 2023, Dr. Li was awarded by National Natural Science Funds for Distinguished Young Scholars.
Spiking Neural Network based Brain-inspired Computingļ¼ From Algorithms to Hardware Architectures
Brain Inspired Computing (BIC) is an emerging research field that aims to build fundamental theories, models, hardware architectures, and application systems toward more general Artificial Intelligence (AI) by learning from the information processing mechanisms or structures/functions of biological nervous systems. It is regarded as one of the most promising research directions for future intelligent computing in the postMoore era. Spiking neural networks (SNNs), a new general of artificial neural networks that more closely mimic natural neural networks, are believed to be potential to explore Brain Inspired Computing. To address the above issues, this talk will focus on how can SNNs benefit from the recent advancements in both deep learning and computational neuroscience.We will focus on discussing the concept of SNNs and summarize four components of BIC infrastructure development: 1) modeling/algorithm; 2) hardware platform; 3) software tool; and 4) benchmark data. We will present a general framework for the real-world applications of BIC systems, which is promising to benefit both AI and brain science.