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Towards a Grammatical Approach to Artificial Intelligence
Date: 2014/3/11             Browse: 720
Speaker: Dr. Kewei Tu (Assistant Professor, SIST, ShanghaiTech University)

Time:      March 11, 2014 (Tuesday), 3:15 to 4:15pm

Location: Room 220, Building 8#


The concept of impedance has once brought a lot of convenience to the ac circuit analysis. Yet, strictly speaking, conventional impedance approach can only be applied to linear time-invariant (LTI) systems; such limitation confined its application in most modern power technologies, where nonlinear components, such as diodes and transistors, are extensively involved. Through extending the vision of this long-standing impedance concept, we explored the revival of impedance approach in two kinds of power conversion systems, including pure electrical Class-E power amplifier (Class-E PA) and electromechanically coupled piezoelectric energy harvesting (PEH) system. The vision extension led to the development of very efficient algorithms for the analysis and optimization of these systems. For instance, Class-E PA optimization based on this technology runs more than 100 times faster than the state of the art. More importantly, integrated analysis of PEH system, in terms of impedance, was achieved for the first time. The holistic insight associated with the proposed integrated analysis helps clarify a series of crucial but previously overlooked or even misunderstood issues in this area. To end this talk, the speaker summarizes his personal insights on interdisciplinary research based on his previous research experiences, in response to the emphasis of interdisciplinary collaboration in ShanghaiTech.


Dr. Kewei Tu received a PhD degree in Computer Science from Iowa State University, USA in 2012. During 2012-2014, he worked as a postdoctoral researcher at the Vision, Cognition, Learning and Art Laboratory, Departments of Statistics and Computer Science of the University of California, Los Angeles, USA. He has been an Assistant Professor with the School of Information Science and Technology at ShanghaiTech University, Shanghai, China since Feb 2014. His main research interests include machine learning of probabilistic grammars with applications in natural language processing and computer vision, and studying grammars as a general model of patterns for different aspects of human cognition. 

SIST-Seminar Series-14003