2022 5th International Conference on Computer and Communication Engineering Technology
19-21 August 2022 – Beijing, China

Keynote Speakers of CCET2021


Prof. Dapeng Oliver Wu

(IEEE Fellow)

University of Florida, USA

Biography: Dapeng Oliver Wu received Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, PA, in 2003. Since 2003, he has been on the faculty of Electrical and Computer Engineering Department at University of Florida, Gainesville, FL, where he is currently Professor. His research interests are in the areas of networking, communications, video coding, image processing, computer vision, signal processing, and machine learning.

He received University of Florida Term Professorship Award in 2017, University of Florida Research Foundation Professorship Award in 2009, AFOSR Young Investigator Program (YIP) Award in 2009, ONR Young Investigator Program (YIP) Award in 2008, NSF CAREER award in 2007, the IEEE Circuits and Systems for Video Technology (CSVT) Transactions Best Paper Award for Year 2001, the Best Paper Award in GLOBECOM 2011, and the Best Paper Award in QShine 2006. He has served as Editor-in-Chief of IEEE Transactions on Network Science and Engineering, and Associate Editor of IEEE Transactions on Communications, IEEE Transactions on Signal and Information Processing over Networks, and IEEE Signal Processing Magazine. He was the founding Editor-in-Chief of Journal of Advances in Multimedia between 2006 and 2008, and an Associate Editor for IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Wireless Communications and IEEE Transactions on Vehicular Technology. He has served as Technical Program Committee (TPC) Chair for IEEE INFOCOM 2012. He was elected as a Distinguished Lecturer by IEEE Vehicular Technology Society in 2016. He is an IEEE Fellow.

Speech Title: Knowledge Centric Networking: Challenges and Opportunities
Abstract: In the creation of a smart future information society, Internet of Things (IoT) and Content Centric Networking (CCN) break two key barriers for both the front-end sensing and back-end networking. However, we still observe the missing piece of the research that dominates the current design, i.e., lacking of the knowledge penetrated into both sensing and networking to glue them holistically. In this talk, I will introduce and discuss a new networking paradigm, called Knowledge Centric Networking (KCN), as a promising solution. The key insight of KCN is to leverage emerging machine learning or deep learning techniques to create knowledge for networking system designs, and extract knowledge from collected data to facilitate enhanced system intelligence and interactivity, improved quality of service, communication with better controllability, and lower cost. This talk presents the KCN design rationale, the KCN benefits and also the potential research opportunities.


Prof. Hai Jin

(IEEE Fellow, CCF Fellow, Changjiang Distinguished Professor)

Huazhong University of Science and Technology, China

Biography: Hai Jin received the PhD degree in computer engineering from Huazhong University of Science and Technology, in 1994. He is a Cheung Kung scholars chair professor of computer science and engineering with Huazhong University of Science and Technology. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. He worked with The University of Hong Kong between 1998 and 2000, and as a visiting scholar with the University of Southern California between 1999 and 2000. His research interests include computer architecture, virtualization technology, cluster computing and cloud computing, peer-to-peer computing, network storage, and network security. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001. He is the chief scientist of ChinaGrid, the largest grid computing project in China, and the chief scientists of National 973 Basic Research Program Project of Virtualization Technology of Computing System, and Cloud Security. He has co-authored 22 books and published more than 700 research papers. He is a fellow IEEE, CCF, and a life member of the ACM.

Speech Title: Towards the Practical Blockchain System: Challenges and Practices
Abstract: Blockchain is the fascinating distributed ledger technology, which holds out the promise of disintermediation, transparency, and openness. An increasing number of businesses, academics and even governments are starting to view blockchain systems as the cornerstone of trust the Web 3.0 era (next generation value Internet). This presentation will first trace the source and the current development status of blockchain systems in various application areas. Secondly, a roadmap of the major theoretical and practical challenging issues faced by these blockchain systems will be laid out. Finally, I will give a glimpse of harnessing the super-abundant opportunities of blockchain systems in the future landscape.



Prof. Tao Zhang   

North China University of Technology, China

Biography: Prof. Tony Zhang is a full professor in College of Information Technology, North China University of Technology (NCUT). He received his PhD degree in computer science from Kent State University of United States America. He was the Chief scientific adviser, North American Headquarters, Volkswagen Group, Germany, the senior banking consultant of IBM banking division, United States, the vice president of technology and operations, Bank of America, the CTO of PRECOM Information Technology, a Silicon Valley company. He founded the Wise-Code Information Technology Co., Ltd and has designed and developed more than 60 software products with independent intellectual property rights, and 100 published papers on the intelligence applications which have been widely used in the fields of intelligence finance and banking, intelligence government, smart education, intelligence medical cares, intelligence manufacturing and intelligence cities and communities, intelligence safety cities, intelligence travel and etc. He is currently the chief expert of the National Invested Top Important Intelligence Projects by Department of National Science and technology, National Recognized and Certified High Level Talents of Oversea Returned Scientist From Western and American, the overseas Senior Technical Adviser of the CBRC, the chief scientist of Beijing Aerospace ChangFeng Co., Ltd., President of Electronic branch of Beijing Expert Association and Vice President of Western Returned Students' Club, the Chairman of International Conference in the Data Signal Process and Artificial Intelligence.

Speech Title:  Intelligent Applications of Loan Risk Control Based on Financial Big Data Technology
Intelligent Technology Applications of Loan Risk Control Based on Financial Big Data, Deep Learning, Machine Learning, Cloud Computation, and Block Chain technology are about how to prepare data warehouse, extract the features of data, draw the precise outline of potential customers who are going to borrow money, and build the models of risk control for borrowers so that lenders can decide can we lend our money to the customer, how much money to be landed to him, how much interest we can take from him before the loans can be granted. How do we control the risk during the loans of the customers, how to manage the risk after the loans. The speech will be talking about the algorithms applied onto the differen phases of processes of loans, data ETL, data classification and cluster analysis, data modelling and analysis, deep learning and machine learning. It will be talked about the architectures of information technology for the workable intelligent loan system implementation, business processes of the loans, strategy of the loans, scoring of borrower behavior, scoring of borrower credits, knowledge base, and decision & inference engine. The contents can be used to design a real system of loans.

Plenary Speakers


Prof. Jiang Xu

The Hong Kong University of Science and Technology, HongKong, China

Biography: Jiang Xu is a full professor at Hong Kong University of Science and Technology (HKUST). He received his PhD from Princeton University and worked at Bell Labs, NEC Labs, and a startup company before joining HKUST. Jiang established Big Data System Lab, Xilinx-HKUST Joint Lab, and OPTICS Lab at HKUST. He currently serves as the Associate Editor for IEEE TCAD, TVLSI, and ACM TECS. He served on the steering committees, organizing committees, and technical program committees of many international conferences, including DAC, DATE, ICCAD, CASES, ICCD, CODES+ISSS, NOCS, HiPEAC, ASP-DAC, etc. Jiang was an IEEE Distinguished Lecturer and an ACM Distinguished Speaker. He authored and coauthored more than 130 book chapters and papers in peer-reviewed international journals and conferences. His research areas include machine learning system, photonic-electronic codesign, optical interconnection network, power delivery and management, MPSoC, low-power embedded system, hardware/software codesign.

Speech Title:  Rejuvenate Post-Moore's Law Computing Systems with Photonics-Electronics Codesign
Computing systems, from HPC and data center to automobile, aircraft, and cellphone, are integrating growing numbers of processors, accelerators, memories, and peripherals to meet the burgeoning performance requirements of new applications under tight cost, energy, thermal, space, and weight constraints. Silicon photonics technologies piggyback onto developed silicon fabrication processes to provide viable and cost-effective solutions. A large number of silicon photonics devices and circuits have been demonstrated in CMOS-compatible fabrication processes. Silicon photonics technologies open up both new opportunities and new challenges to applications, architectures, design techniques, and design automation tools for hybrid photonics-electronics information systems. Based on our decade-long quest to transform computing systems with silicon photonics, this talk tries to answer several key questions. How could computing systems benefit from silicon photonics technologies? What technologies are required? And what are the challenges?


Prof. Alexei Shishkin

Moscow State University, Russia

Biography: Alexei G. Shishkin received the M.Sc. degree in applied mathematics from the Moscow State University, Moscow, Russia, in 1987, and the Ph.D. degree in mathematical modeling from Moscow State University, Moscow, Russia, in 1990. He received his D.Sc. degree in computational mathematics and mathematical modeling from Moscow State University, Moscow, Russia, in 2011. He was a Senior Research Worker and then Chief Research Worker with the Department of Computational Mathematics and Cybernetics, Moscow State University, starting from 1990. He is currently a Professor with the Department of Computational Mathematics and Cybernetics, Moscow State University. His current research interests include mathematical modeling, machine learning, data mining and digital signal processing. Prof. Shishkin is an author of more than 150 publications including 6 books. He is a member of Editorial Board of Journal of Modeling and Optimization and a reviewer of IEEE Transactions on Cybernetics. Prof. Shishkin has been a member and a chairman of the program committees of a number of international conferences.

Speech Title:  Deep Learning for Human Emotional State Recognition
Human emotions play significant role in everyday life. Therefore, recognizing emotional states is crucial to understand human behaviour, cognition and decision making. Today, there are a large number of methods for measuring emotions and analyzing the physiological data. These methods differ in their potential with regard to what emotions can be detected, their accuracy, the ability to verify the results, and their applicability in different circumstances. In recent years, deep learning techniques have played a decisive role in data processing. The aim of this work is to give an overview of deep learning methods to recognize emotions based on different inputs such as biometrics, video and audio signals, etc. All aspects of human emotions recognition by deep learning methods such as the selection of neural architecture to use, sensor modalities, existing datasets, features and range of expected accuracy are considered. Besides, a new deep learning system for real-time human emotion recognition by audio-visual data is proposed. The advantage of the system is its robustness and real-time performance.