报 告 内 容 简 介 |
报告内容简介： This talk introduces the fundamentals of Spiking Neural Networks (SNNs), covering neuron dynamics, encoding schemes, and training approaches. We then discuss how LIF-based spiking systems can be interpreted as iterative optimization processes, enabling efficient solutions to quadratic and linear programs. The framework offers practical benefits for real-time robotic control and embedded computation. This talk will introduce how Dr. Khan is incorporating this framework in his research toward developing efficient software and hardware platforms to enable energy efficient real-time intelligent control of robotic system.
报告人简介： Dr. Ameer H. Khan is a Professor at Taizhou University, where he is conducting research at the intersection of robotics, artificial intelligence, and efficient computing technologies. His work spans Optimization and Control theory, applied for AI-driven autonomous robotics, navigation. Dr. Khan earned his Ph.D. in Computing Systems from PolyU, developing an RNN-based optimal control framework for hybrid robotic agents. He has authored more than 20 peer-reviewed publications, in several IEEE Transactions and other reputable journals. He is also co-author of a Springer book on optimization-driven decision-making in complex systems. Beyond research, Dr. Khan has served as guest editor for special issues in Biomimetics, IEEE JSAS, and Digital Finance, and delivered invited talks internationally. |
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