ICIC 2022 Plenary Speakers

  • Vladimir Filaretov
  • Irwin King
  • Dacheng Tao
  • Zhiwen Yu
    •

    Vladimir Filaretov
    PhD & Professor
    Academicians of Russian Engineering Academy and Russian Science Academy
    Vice-president of Russian Engineering Academy, Vladivostok, Russia
    Irwin King
    Ph.D & Professor
    IEEE Fellow, INNS Fellow, ACM Distinguished Member
    The Chinese University of Hong Kong, China
    Dacheng Tao
    PhD & Professor
    Fellow of the Australian Academy of Science, IEEE Fellow, ACM Fellow
    Director of JD Explore Academy
    Zhiwen Yu
    PhD & Professor
    Editor-in-Chief, CCF Transactions on Pervasive Computing and Interaction, CCF Young Scientist Award
    Northwestern Polytechnical University, China

    Methods of recognition and information processing for intelligent control of various robots

    Vladimir Filaretov, PhD & Professor
    Academicians of Russian Engineering Academy and Russian Science Academy
    Vice-president of Russian Engineering Academy, Vladivostok, Russia
    Head of Robotics Laboratory at Institute of Automation and Control Processes Far Eastern Branch of Russian Academy of Science
    Head of the Department of robotics and Automation at Far Eastern Federal University
    Member of Presidium of the Highest Engineering Council of Russia
    Email:filaretov@inbox.ru

    Abstract:The talk is dedicated to creation technologies of intelligent control systems of various robots which can automatically perform complex technological operations in non-deterministic operating environment. These systems are constructed based on information processing, obtained from different vision systems, and provide automatically generation and correction of robot's motion trajectory in a priori unknown and changeable environment. For realization of these systems, a different method of recognition and processing of information obtained from vision systems (optical and laser) will be presented. Here I will talk about method of fast combination of three-dimensional models of deformed parts obtained from laser scanners with their reference CAD-models. Based on this combination it is possible to make trajectory planning of robots in real time for exact processing of the parts. For mobile robots I will present new algorithm for combining images into a one whole raster photo map from a sequence of individual images or video frames using tile graphics and simple transformations of input images. The use of tiles allows to present the generated map in a convenient form both for a person and for the on-board control system of the robot.

    Bio-Sketch:Vladimir Filaretov was born in 1948. In 1973 graduated from Moscow State Technical University named after Bauman with honors with the specialty “Automatic systems”. In 1976 Mr. Filaretov was awarded the degree of candidate of sciences (engineering) and in 1990 he was awarded the degree of Doctor of Sciences in the field of automatic control. In 1992 Mr. Filaretov was confirmed in professor’s degree. In 1995 he was elected the member of an Russian and in 1996 the member of an International Engineering Academy. At present he is head of Department of Robotics and Automation at Far Eastern Federal University and Head of Robotic Laboratory of the Institute of Automatics and Control Process of Russian Academy of Sciences, President of Far Eastern Branch Russian Engineering Academy and Vice-president of Russian Engineering Academy. Professor Vladimir Filaretov is a specialist in the field of adaptive and optimal control devices of complicated nonlinear systems of automatic control with unknown and variable parameters, and also in the field of mathematical description of complicated multi-connected mechanisms dynamics. His researches are mainly directed at creation both industrial and underwater robots and manipulators and also other dynamic systems, allowing to automate technical devices and technological processes. Professor V. Filaretov has more than 640 scientific publications, 10 monographs and 330 patents (inventions) for developed technical systems and devices.


    Graph Neural Networks from Theory to Applications

    Irwin King, PhD & Professor
    IEEE Fellow, INNS Fellow, ACM Distinguished Member
    Department of Computer Science & Engineering
    The Chinese University of Hong Kong, China
    Email:king@cse.cuhk.edu.hk

    Abstract:Graph Neural Network (GNN) is a neural network that can process graph-structured data such as social networks, citation networks, traffic networks, semantic networks, polygon mesh, molecular structures, etc. In addition to non-Euclidean data, GNN can also handle Euclidean data such as sentences, images, and videos. The main approach is through graph embedding, which refers to the problem of projecting the elements in a graph, including nodes, edges, substructures, or the whole graph, to a low-dimensional space while preserving the graph’s structural information. In this talk, we present some recent advances in the development of GNN including graph embedding, convolution-based methods, attention networks, etc. with some interesting social computing applications for node classification, link prediction, social recommendation, etc.

    Bio-Sketch:Prof. Irwin King is the Chair and Professor of Computer Science & Engineering at The Chinese University of Hong Kong. His research interests include machine learning, social computing, AI, and data mining. He has over 350 technical publications in journals and conferences in these research areas with high citations. He is an IEEE Fellow, INNS Fellow, and an ACM Distinguished Member. He is an Associate Editor of the Journal of Neural Networks (NN) and has served as the President of the International Neural Network Society (INNS), General Co-chair of WebConf 2020, ICONIP 2020, WSDM 2011, RecSys 2013, ACML 2015, and in various capacities in top conferences and societies such as WWW, NIPS, ICML, IJCAI, AAAI, APNNS, etc. He is the recipient of several Test of Time Awards including ACM CIKM2019, ACM SIGIR 2020, and ACM WSDM 2022 for his contributions made in social computing with machine learning. He also won the 2021 INNS Dennis Gabor Award for his outstanding contributions to engineering applications of neural networks. In early 2010 while on leave with AT&T Labs Research, San Francisco, he taught classes as a Visiting Professor at UC Berkeley. He received his B.Sc. degree in Engineering and Applied Science from the California Institute of Technology (Caltech), Pasadena, and his M.Sc. and Ph.D. degree in Computer Science from the University of Southern California (USC), Los Angeles.


    More Is Different: ViTAE elevates the art of computer vision

    Dacheng Tao, PhD & Professor
    Fellow of the Australian Academy of Science, IEEE Fellow, ACM Fellow
    Director of JD Explore Academy
    Email:dacheng.tao@gmail.com

    Abstract:Deep learning has witnessed remarkable success in many application domains and is now shifting towards training super deep models with extremely large scale labeled or unlabeled data on expensive computational resources. In this talk, I will present some of the recent progress. Specifically, I will first show the PAC-Bayes generalization bounds and present some practical implications for new algorithm designs. Then, I will propose an efficient architecture design for visual transformers, named ViTAE, by exploring the intrinsic inductive biases. Next, he will introduce a novel self-supervised training method called RegionCL, which uses a simple region swapping strategy to build effective supervisory signals from rich positive/negative pairs at both the instance level and the region level. It greatly advances the ability of representative self-supervised leaning frameworks including MoCo, SimCLR, and SimSam. Finally, some promising applications of visual transformers and self-supervised leaning will be presented, including image classification, object detection, semantic segmentation, and pose estimation.

    Bio-Sketch:Dacheng Tao is the Inaugural Director of the JD Explore Academy and a Senior Vice President of JD.com. He is also an advisor and chief scientist of the digital science institute in the University of Sydney. He mainly applies statistics and mathematics to artificial intelligence and data science, and his research is detailed in one monograph and over 200 publications in prestigious journals and proceedings at leading conferences. He received the 2015 Australian Scopus-Eureka Prize, the 2018 IEEE ICDM Research Contributions Award, and the 2021 IEEE Computer Society McCluskey Technical Achievement Award. He is a fellow of the Australian Academy of Science, the World Academy of Sciences, the Royal Society of NSW, AAAS, ACM, IAPR and IEEE.


    Crowd Sensing 2.0: From Human-Centered to Heterogeneous Crowd Sensing

    Zhiwen Yu, PhD & Professor
    Editor-in-Chief, CCF Transactions on Pervasive Computing and Interaction
    CCF Young Scientist Award, CCF Excellent Doctoral Dissertation Award
    School of Computer Sciences at Northwestern Polytechnical University, China
    Email:zhiwenyu@nwpu.edu.cn

    Abstract:Crowd sensing is a new sensing paradigm that uses individual sensing capability to accomplish complex social sensing tasks. In this speech, I will introduce our work in crowd sensing in various aspects, such as theory, methods, and platform. Furthermore, I will present the main idea of crowd sensing 2.0, including the featues and enabling technologies of heterogeneous crowd sensing.

    Bio-Sketch:Dr. Zhiwen Yu is currently a professor and dean of School of Computer Science, Northwestern Polytechnical University, China. He has worked as an Alexander Von Humboldt Fellow at Mannheim University, Germany from Nov. 2009 to Oct. 2010, a research fellow at Kyoto University, Japan from Feb. 2007 to Jan. 2009, and a post-doctoral researcher at Nagoya University, Japan in 2006-2007. His research interests cover pervasive computing, Internet of Things, and mobile social networks. He is the Editor-in-Chief of CCF Transactions on Pervasive Computing and Interaction. He has served as an associate/guest editor for a number of international journals, such as IEEE Transactions on Human-Machine Systems, IEEE Communications Magazine, and ACM Transactions on Intelligent Systems and Technology. He received the CCF Young Scientist Award in 2011, the Humboldt Fellowship in 2008, and the CCF Excellent Doctoral Dissertation Award in 2006. He got the National Science Fund for Distinguished Young Scholars in 2017.