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
Abstract:
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.
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
Abstract:
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
Abstract: 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.