| Professor Liming ChenL'Ecole Centrale de Lyon, FranceProf. Liming Chen was awarded a joint BSc degree in Mathematics and Computer Science from the University of Nantes in 1984. He obtained a Master degree in 1986 and a PhD in computer science from the University of Paris 6 in 1989. He first served as associate professor at the Université de Technologie de Compiègne, then joined Ecole Centrale de Lyon as Professor in 1998, where he leads an advanced research team on multimedia computing and pattern recognition. From 2001 to 2003, he also served as Chief Scientific Officer in a Paris-based company, Avivias, specialized in media asset management. In 2005, he served as Scientific multimedia expert in France Telecom R&D China. He has been Head of the department of Mathematics and Computer science from 2007. Prof. Liming Chen has taken out 3 patents, authored more than 100 publications and acted as chairman, PC member and reviewer in a number of high profile journal and conferences since 1995. He has been a (co)-principal investigator on a number of research grants from EU FP programme, French research funding bodies and local government departments. He has directed more than 15 PhD theses. His current research spans from 2D/3D face analysis and recognition, image and video analysis and categorization, to affect analysis both in image, audio and video. Speech Title:TBD Abstract: TBD |
| Professor Maozhen LiDept.of Electronic and Electrical Engineering Brunel University ofLondon Uxbridge,UB83PH,UKMaozhen Li received the Ph.D. degree from the Institute of Software, Chinese Academy of Sciences, Beijing, China, in 1997. He did his Post-Doctoral research in the Department of Computer Science at Cardiff University UK in 1999-2002. He is a Professor with the Department of Electronic and Electrical Engineering, Brunel University of London, UK. His main research interests include high-performance computing, big data analytics, and intelligent systems with applications to smart grids, smart manufacturing and smart cities. He has about 240 research publications in these areas, including four books. His book entitled “The Grid: Core Technologies” was introduced by Tsinghua publisher as a classical textbook on Grid computing. He is a Fellow of the British Computer Society (BCS) and the Institute of Engineering and Technology (IET). He has served over 30 IEEE conferences and serves on the editorial board for a number of journals. His research work on Big Data was shortlisted by Computing in May 2018 for BIG DATA EXCELLENCE AWARDS in the category of Most Innovative Big Data Solution. His recent research on a computation efficient AI model outperforms the two pioneering works in this field – GhostNet and MobileNet. This work is now available on IEEE Transactions on Neural Networks and Learning Systems. Speech Title:InterpretationandComputationEfficiencyinDeepNeuralNetworks Abstract: The past two decades have witnessed a tremendous success of AI applications in many areas mainly due to the rapid development of sophisticated deep neural networks (DNNs). However, DNNs normally work in a black-box style, making it challenging for deployment of DNNs in life critical situations such as autonomous driving where safety has to be guaranteed. This talk starts with a brief review on interpretation methods of DNNs based on which it presents SA-CAM, generating self-attention activation maps for visual interpretations of CNNs. Further down the line, this talk focuses on computation efficient lightweight AI models which can potentially be deployed on resource constrained mobile devices. Specifically, it presents CEModule, a computation efficient module for lightweight CNNs through model interpretation. Towards the end, this talk touches upon computation intensive heavyweight AI models like ChatGPT and brings up some discussions on their challenges. |
