Human brain can perform many tasks much better than classical electronic computers, such as face recognition, reasoning based on vague information, and learning from experience, to name a few. Recently, brain-inspired algorithms have promoted in the rapid development of artificial intelligence, however, they cannot work well in classical computers. In this talk, Dr. Can Li will present his recent works on building brain-inspired computers to fit better with brain-inspired algorithms. Those computers are based on an emerging nanoelectronics device – a memristor – which can store information and compute simultaneously, similar to synapses and neurons in our brain. The built hardware can function similar to human brains, for example, it can tolerate hardware defects, make full use of the nonlinearity of devices, learn from rare samples, and so on.
Dr. Can Li is currently an Assistant Professor at the Department of Electrical and Electronic Engineering of the University of Hong Kong, working on analog and neuromorphic computing accelerators based on post-CMOS emerging devices (e.g., memristors) for efficient machine/deep learning, network security, signal processing, etc. Before that, He spent two years at Hewlett Packard Labs in Palo Alto, California, and obtained his Ph.D. from University of Massachusetts, Amherst, and B.S./M.S. from Peking University. He is a recipient of the Early Career Award by HKSAR RGC and the Excellent Young Scientist Fund Award by NSFC.
Clean water and clean air are vital for public health. This project focuses on developing high-efficiency and environmentally sustainable filters for removing harmful air/water pollutants. The team has developed novel architectures and functionalities for the filters to achieve high permeance, high removal efficiency, and excellent reusability.
