CCID 2023 provides a premier venue for the presentation and discussion of research in the design, development, deployment and evaluation of cloud computing and intelligent driving. It bridges together academic researchers and industrial practitioners to share and exchange the latest developments in the area of high-performance computing and autonomous driving. We hope that this conference will stimulate our participants to explore innovative advances and applications in computing science and artificial intelligence.
We gratefully acknowledge the support from University of Macau, Macau Science and Technology Development Fund (FDCT) and Ministry of Science and Technology of China. We wish to express our heartfelt appreciation to the keynoter speakers, invited speakers, students and volunteers for their help. We wish all participants and colleagues a very pleasant stay in Macau.
Prof. Cheng-Zhong Xu, Chair Professor, University of Macau
Prof. Huanle Xu, Assistant Professor, University of Macau
Prof. Shuai Wang, Associate Professor, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Prof. Hui Kong, Associate Professor, University of Macau
Prof. Leong Hou U, Associate Professor, University of Macau
Prof. Juanjuan Zhao, Assoicate Professor, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
Prof. Li Li, Assistant Professor, University of Macau
Prof. Zhenning Li, Assistant Professors, University of Macau
08:30 - 08:45
Prof. Cheng-Zhong Xu, University of Macau
Prof. Kejiang Ye, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
09:00 - 09:55
Prof. Xiaoming Fu, University of Göttingen
09:55 - 10:50
Prof. Hai Jin, Huazhong University of Science and Technology (HUST)
11:10 - 11:40
Prof. Xiaowen Chu, Hong Kong University of Science and Technology (Guangzhou)
11:55 - 12:15
Prof. Quan Chen, Shanghai Jiao Tong University
12:20 - 14:00
14:00 - 14:30
Dr. Wei Sui, Horizon Robotics
15:55 - 16:25
Prof. Wenchao Ding, Fudan University
09:00 - 09:55
Prof. Xiang-Gen Xia, University of Delaware
09:55 - 10:50
Prof. Tomoyuki Yokota, University of Tokyo
11:10 - 11:40
Prof. Zhi Zhou, Sun Yat-sen University
14:00 - 14:30
Prof. Fu Zhang, University of Hong Kong
14:30 - 15:00
Prof. Haipeng Dai, Nanjing University
Panel Chair: Prof. Song Guo, The Hong Kong University of Science and Technology
Prof. Jianping Wang, City University of Hong Kong
Prof. Song Jiang, University of Texas at Arlington
Prof. Xiaowen Chu, The Hong Kong University of Science and Technology (Guangzhou)
Prof. Chuan Wu, University of Hong Kong
Prof. Huanle Xu, University of Macau
09:00 - 09:55
Prof. Roger Wattenhofer, ETH Zurich
09:55 - 10:25
Prof. Chuan Wu, University of Hong Kong
10:25 - 10:55
Prof. Cong Yang, Soochow University
Prof. Roger Wattenhofer
Full professor, ETH Zurich
Learning with Graphs
Abstract: Graphs serve as a fundamental representation for large and intricate data sets. Their significance transcends various domains; in the natural sciences, graphs serve as a means to depict complex structures such as molecules, proteins, and genomes. In mathematics, they find utility in representing algebraic groups and knots. Additionally, graphs are indispensable tools for modeling social networks, traffic patterns, and they exhibit a plethora of applications within the realm of computer science.
Roger Wattenhofer is a full professor at the Information Technology and Electrical Engineering Department, ETH Zurich, Switzerland. He received his doctorate in Computer Science from ETH Zurich. He also worked multiple years at Microsoft Research in Redmond, Washington, at Brown University in Providence, Rhode Island, and at Macquarie University in Sydney, Australia. Roger Wattenhofer’s research interests include a variety of algorithmic and systems aspects in computer science and information technology, e.g., distributed systems, positioning systems, wireless networks, mobile systems, social networks, financial networks, deep neural networks. He publishes in different communities: distributed computing (e.g., PODC, SPAA, DISC), networking and systems (e.g., SIGCOMM, SenSys, IPSN, OSDI, MobiCom), algorithmic theory (e.g., STOC, FOCS, SODA, ICALP), and more recently also machine learning (e.g., ICML, NeurIPS, ICLR, ACL, AAAI). His work received multiple awards, e.g. the Prize for Innovation in Distributed Computing for his work in Distributed Approximation. He published the book “Blockchain Science: Distributed Ledger Technology“, which has been translated to Chinese, Korean and Vietnamese.
Prof. Xiang-Gen Xia
Charles Black Evans Professor, University of Delaware
Linear Processing for Vector-Valued Signals with Vector-Valued Coefficients
Abstract: In this talk, we first introduce arithmetics (+, ―, ×, ÷) for real vectors of any fixed dimension, which are similar to those for real numbers. It can be thought of as a generalization of complex numbers that are just two dimensional real vectors. This is based on rational vector approximations and arithmetics for rational vectors, and can be done via algebraic number fields. Then, we introduce complex conjugate for a real vector and inner product for two real vectors and two real vector-valued signals of finite length. We also define convolution of two real vector-valued signals of finite length. With these concepts, the conventional linear filtering, least squares fitting, and ARMA model for scalar-valued signals with scalar-valued coefficients can be easily generalized to real vector-valued signals with real vector-valued coefficients, which broadens the existing linear signal processing methods for scalar-valued signals and might open a door to the future signal processing.
Xiang-Gen Xia is the Charles Black Evans Professor, Department of Electrical and Computer Engineering, University of Delaware, Newark, Delaware, USA. Dr. Xia was the Kumar’s Chair Professor Group Professor (guest) in Wireless Communications, Tsinghua University, during 2009-2011, the Chang Jiang Chair Professor (visiting), Xidian University, during 2010-2012, and the World Class University (WCU) Chair Professor (visiting), Chonbuk National University, during 2009-2013. He received the National Science Foundation (NSF) Faculty Early Career Development (CAREER) Program Award in 1997, the Office of Naval Research (ONR) Young Investigator Award in 1998, the Outstanding Overseas Young Investigator Award from the National Nature Science Foundation of China in 2001, and the Information Theory Outstanding Overseas Chinese Scientist Award from the Chinese Information Theory Society of Chinese Institute of Electronics in 2019. Dr. Xia was the General Co-Chair of ICASSP 2005 in Philadelphia. He is a Fellow of IEEE. His current research interests include space-time coding, MIMO and OFDM systems, digital signal processing, and SAR and ISAR imaging. He is the author of the book Modulated Coding for Intersymbol Interference Channels (New York, Marcel Dekker, 2000) and a co-author of the book Array Beamforming Enabled Wireless Communications (New York, CRC Press, 2023).
Prof. Hai Jin
Chair Professor, Huazhong University of Science and Technology
Dataflow based High Efficient Graph Processing Accelerator
Abstract: With the rapid growth of big data, it is harder and harder to processing these ever-growing data with traditional computer architecture. Dataflow-based architecture provides a new way to tackle above challenge. This talk first briefly introduce the challenges in processing big data and also the difficulties in processing graph computing, then introduce some research results we have done during these years in using dataflow for graph computing. Finally, some future directions for dataflow architecture and also when used in graph computing are introduced.
Hai Jin is a Chair Professor of computer science and engineering at Huazhong University of Science and Technology (HUST) in China. Jin received his PhD in computer engineering from HUST in 1994. In 1996, he was awarded a German Academic Exchange Service fellowship to visit the Technical University of Chemnitz in Germany. Jin worked at The University of Hong Kong between 1998 and 2000, and as a visiting scholar at the University of Southern California between 1999 and 2000. He was awarded Excellent Youth Award from the National Science Foundation of China in 2001.
Prof. Xiaoming Fu
Chair Professor, University of Göttingen
Networking Implications of Video Analytics
Abstract: With the tremendous growth of Internet videos and their analytics techniques, various challenges emerge particularly those concerning mobility and analytics efficiency. How to mitigate these challenges and how can we embrace the opportunities in this era? In this talk, I will introduce some of our recent efforts and outline a vision towards integrating sensing, networking and data analytics in a supporting infrastructure in the digital world.
Prof. Xiaoming Fu received his Ph.D. in computer science from Tsinghua University, Beijing, China in 2000. He was then a research staff at the Technical University Berlin until joining the University of Göttingen, Germany in 2002 as assistant professor, where he has been a full professor of computer science and heading the Computer Networks Group since 2007. He has spent research visits at universities of Cambridge, Columbia, UCLA, Tsinghua, Nanjing, Fudan, Uppsala, UPMC, and Sydney universities.
Prof. Tomoyuki Yokota
Associate Professor, University of Tokyo
Ultra-flexible organic electronics for health monitoring
Abstract: Optical bio-imaging is a non-invasive method to measure biological information from outside of the body, such as fluorescent probes, photoacoustic imaging, and near-infrared spectroscopy (NIRS) have been widely used as medical devices. In recent years, along with the development of semiconductor technology, the miniaturization of these imaging devices has been progressing. In particular, organic optical devices has featured characteristics such as high efficiency , flexibility , and lightweight , and are being actively applied to healthcare by integrating them into wearable devices .
Tomoyuki Yokota was born in Tochigi, Japan, in 1985. He received the B.S., M.S., and Ph.D. degrees in applied physics from the University of Tokyo, Tokyo, Japan, in 2008, 2010, and 2013, respectively. From 2013 to 2016, he was a Project Assistant Professor with the Department of Electrical and Electronic Engineering, University of Tokyo, where he has been a Lecturer since 2016. Since 2019, he has been an associate professor at the Department of Electrical and Electronic Engineering, University of Tokyo. His current research interests include organic photonic devices, flexible electronics, printed electronics, large-area sensors, and wearable electronics.
Prof. Chuan Wu
Professor, Hong Kong University
Optimizing Distributed DNN Training in AI clouds
Prof. Xiaowen Chu
Professor, Hong Kong University of Science and Technology (Guangzhou)
Benchmarking and Dissecting the Nvidia Hopper GPU Architecture
Prof. Quan Chen
Professor, Shanghai Jiao Tong University
Towards low latency serverless computing for complex applications
Prof. Haipeng Dai
Assoicate professor, Nanjing University
Prof. Zhi Zhou
Assoicate professor, Sun Yat-sen University
Towards Cost-Efficient DNN Model Training and Inference Serving with Serverless Pipelines
Dr. Wei Sui
Senior Engineer, Horizon Robotics
Visual 3D Reconstruction in Autonomous Driving
Prof. Wenchao Ding
Assoicate professor, Fudan University
Advanced Scene understanding and Decision-making for Autonomous Driving
Prof. Cong Yang
Assoicate professor, Soochow University
Human-Computer Interaction in Intelligent Cockpit
Prof. Fu Zhang
Assistant Professor, University of Hong Kong
Lidar-based autonomous UAVs