Welcome Prof. Sun-Yuan Kung, Princeton University, USA (Life Fellow of IEEE) to be the keynote speaker!


Prof. Sun-Yuan Kung, Princeton University, USA (Life Fellow of IEEE)

Prof. Sun-Yuan Kung, IEEE Fellow, distinguished lecturer of IEEE Signal Processing Society and honorary professorship of Central China Science & Technology University. He has received the BS degree in electrical engineering from National Taiwan University, China, in 1971, and the MS degree from University of Rochester in 1974 and the PhD degree from Stanford University in 1977. He was the Sino-US Exchange Scientist, National Academy of Science in 1987. And he was awarded the IEEE Signal Processing Society Best Paper Award and Technical Achievement Award and IEEE Third Millennium Medal. In 2016, he was also honored as a Life Fellow of IEEE for his contributions to VLSI signal processing and neural networks. Now he is the professor of Electrical and Computer Engineering, Princeton University. His research focus on developing high-performing learning networks. They have incorporated a notion of Internal Neuron's Learnablility (INL) into the traditional external learning paradigm (i.e. BP) and create a new generation of neural networks, called Explainable Neural Network (XNN). In addition, the new XNN model opens up a promising machine learning research front for Internal Neuron's Explainablility (INE), a key ingredient in DARPA’s Explainable AI (sometimes referred to as AI3.0).