Dr. Cheng-Zhong Xu, IEEE Fellow, is the Dean of Faculty of Science and Technology and the Interim Director of Institute of Collaborative Innovation, University of Macau, and a Chair Professor of Computer and Information Science. He was a professor of Wayne State University and the Director of Institute of Advanced Computing of Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences before he joined UM in 2019. Dr. Xu is a Chief Scientist of Key Project on Smart City of MOST, China and a Principal Investigator of the Key Project on Autonomous Driving of FDCT, Macau SAR. Dr. Xu’s main research interests lie in parallel and distributed computing and cloud computing, in particular, with an emphasis on resource management for system’s performance, reliability, availability, power efficiency, and security, and in big data and data-driven intelligence applications in smart city and self-driving vehicles. The systems of particular interest include distributed systems and the Internet, servers and cloud datacenters, scalable parallel computers, and wireless embedded devices and mobile edge systems. He published two research monographs and more than 300 peer-reviewed papers in journals and conference proceedings; his papers received about 10K citations with an H-index of 52. He was a Best Paper Nominee or Awardee of the 2013 IEEE High Performance Computer Architecture (HPCA), the 2013 ACM High Performance Distributed Computing (HPDC), IEEE Cluster’2015, ICPP’2015, GPC’2018, UIC’2018, AIMS’2019. He also received more than 100 patents or PCT patents and spun off a business “Shenzhen Baidou Applied Technology” with dedication to location-based services and technologies. Dr. Xu received the most prestigious “President’s Awards for Excellence in Teaching” of Wayne State University in 2002. He serves or served on a number of journal editorial boards, including IEEE Transactions on Computers (TC), IEEE Transactions on Cloud Computing (TCC), IEEE Transactions on Parallel and Distributed Systems (TPDS), Journal of Parallel and Distributed Computing (JPDC), Science China: Information Science and ZTE Communication. Dr. Xu has been the Chair of IEEE Technical Committee on Distributed Processing (TCDP) since 2015. He obtained BSc and MSc degrees from Nanjing University in 1986 and 1989 respectively, and a PhD degree from the University of Hong Kong in 1993, all in Computer Science and Engineering.

Please see https://fst.um.edu.mo/personal/czxu for personal webpage

Research

Dr. Xu’s research is on the interactions of computer systems and applications. It studies systems support for emergent applications and investigates novel ways of design and implementation of applications enabled by latest advance of systems.

  • As computer systems become more and more networked and complex, new foundations are needed for understanding and controlling their integral properties. Dr. Xu’s research on one hand is dedicated to the investigation, establishment, and experimental evaluation of new theoretical foundations and system artifacts to significantly improve the systems performance, availability, security, and energy efficiency. Recent work focused on resource management in cloud datacenters, edge computing infrastructure, and 5G-enabled IoT systems including self-driving vehicles.
  • On the other hand, Dr. Xu’s research looks into smart city applications and intelligent transportation, in particular, and develops autonomous, pervasive, privacy-preserving technologies for data analytics and data-driven intelligence. Recent emphasis is on automatic machine learning, and deep federated reinforcement learning for smart city and autonomous driving.

Research Group

Dr. Xu’s research team is comprised of an elite group of students and research associates, who actually conduct the nuts and bolts of the research. Most of his former PhD students and post-docs have escaped to academic positions or major industrial labs. For information about current research team, please refer to State Key Laboratory of Internet of Things for Smart City and Center for Cloud Computing for details. Dr. Xu continuously looks for self-motivated and hard-working students to join force. Financial support is available for those who have demonstrated a passion for their work.

Representative Work in Systems Support for AI and Big Data Applications (show list | hide)

  • N. Tziritas, et al. Online live VM migration algorithms to minimize total migration time and downtime, IEEE IPDPS’2019.
  • G. Xu and C. Xu, MEER: online estimation of optimal memory reservations for long lived containers in in-memory cluster computing. IEEE ICDCS’2019.
  • X. Gao*, Y. Zhao*(equal), et al, Dynamic Channel Pruning: Feature Boosting and Suppression. Proc. of ICLR’2019.
  • Y. Zhao*, X. Gao*(equal), et al. Mayo: A Framework for Auto-generating Hardware Friendly Deep Neural Networks. In MobiSys EMDL workshop, Jun 2018.
  • R. Li, C. Shen, H. He, Z. Xu and C. Xu. A lightweight secure data sharing scheme for mobile cloud computing. IEEE Transactions on Cloud Computing, April 2018.
  • Y. Wang,  et al, Data Caching in Next Generation Mobile Cloud Services, Online vs. Off-Line. Proc. of ICPP 2017: 412-421.
  • N. Tziritas, et al, Data replication and virtual machine migrations to mitigate network overhead in edge computing systems. IEEE Transactions on Sustainable Computing, June 2017.
  • S. He, et al, Heterogeneity-aware collective I/O for parallel I/O systems with hybrid HDD/SSD servers, IEEE Transactions on Computers, 66(6):1091-1098, 2017.
  • S. He, et al, HARL: optimizing parallel file systems with heterogeneity-aware region-level data layout. IEEE Transactions on Computers, 66(6):1048-1060, June 2017.
  • Y. Wang, et al. On service migrations in the cloud for mobile accesses: a distributed approach, ACM Transactions on Autonomous Adaptive Systems, 12(2):1-25, May 2017.
  • L. Zeng, et al, Raccoon: a novel network I/O allocation framework for workload-aware VM scheduling in virtual environments. IEEE Trans. on Parallel and Distributed Systems, 28(9):2651-2662, Sept 2017.
  • S. He, et al, Using minmax-memory claims to improve in-memory workflow computations in the cloud, IEEE Trans. on Parallel and Distributed Systems, 28(4):1202-1214, April 2017.
  • T. Maqsood, et al, Leveraging on deep memory hierarchies to minimize energy consumption and data access latency on single-chip cloud computers. IEEE Transactions on Sustainable Computing, 2(2):154-166, February 2017.
  • L. Zeng, S. Xu, Y. Wang, K. Kent, D. Bremner, C. Xu, Toward cost-effective replica placements in cloud storage systems with QoS-awareness. Software: Practice and Experience, 47(6):813-829, June 2017.
  • Y. Liu, et al, Barrier-Aware warp scheduling for throughput processor, ACM Int. Conf. on Supercomputing (ICS), June 2016.
  • G. Xu, C. Xu and S. Jiang. Prophet: Scheduling Executors with Time-Varying Resource Demands on Data-Parallel Computation Frameworks. Proc. ICAC 2016: 45-54.
  • D. Dilli, et al, A low disk-bound transaction logging system for in-memory distributed data stores, IEEE Proc. of Cluster Computing , September 2016 (Best paper award nominee).
  • N. Tziritas, et al, On improving constrained single and group operator placement using evictions in big data environments, IEEE Transactions on Service Computing, Sept 2016.
  • Z. Bei, et al, RFHOC: A random-forest approach to auto-tuning Hadoop’s configuration. IEEE Trans. on Parallel and Distributed Systems, 27(5): 1470-1483, May 2016.
  • C. Jiang, et al, Two-Level Hybrid Sampled Simulation of Multithreaded Applications. ACM Transactions on Architecture and Code Optimization (TACO), 12(4): 39:1-39:25, April 2016.
  • C. Liu, et al. Strategy configurations of multiple users competition for cloud service reservation. IEEE Trans. on Parallel and Distributed Systems. 27(2):508-520, February 2016.

Representative Work in Intelligent Transportation and Smart City, and Data Intelligence (show list | hide)

  • H. Xiong*, K. Wang*(equal), et al, SpHMC: Spectral Hamiltonian Monte Carlo, Proc. of AAAI, 2019.
  • L. Yan, et al, Employing opportunistic charging for electric taxicabs to reduce idle time, Proc of ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), vol 2(1), 2018 (Proc. of ACM on Ubicomp, 2018).
  • H. Zhang, et al, Urban-Scale Human Mobility Modeling With Multi-Source Urban Network Data. IEEE/ACM Transactions on Networking 26(2): 671-684 (2018).
  • Z. Yu, et al, MIA: Metric importance analysis for big data workload characterization, IEEE Trans. on Parallel and Distributed Systems, 29(6):1371-1384, June 2018.
  • L. Yan, et al. CatCharger: Deploying wireless charging lanes in a metropolitan road network through categorization and clustering of vehicle traffic. INFOCOM 2017:1-9.
  • B. Zhao, et al, A data-driven congestion diffusion model for characterizing traffic in metrocity scales, Proc. of IEEE Conf. on Big Data, 2017.
  • J. Zhao, et al, Estimation of passenger route choice pattern using smart card data for complex metro systems. IEEE Trans. on Intelligent Transportation Systems, 18(4):790-801, April 2017.
  • Y. Zhao, et al, ER-CRLB: An Extended Recursive Cramér-Rao Lower Bound Fundamental Analysis Method for Indoor Localization Systems, IEEE Transactions on Vehicular Technology, 66(2):1605-1618, February 2017.
  • F. Zhang, et al. Spatiotemproal segmentation of metro trips using smart card data. IEEE Transactions on Vehicular Technology, 65(3):1137-1149, March 2016.
  • F. Tang, M. Guo, S. Guo, C. Xu. Mobility prediction based joint stable routing and channel assignment for mobile ad hoc cognitive networks. IEEE Trans. on Parallel and Distributed Systems, 27(3): 789-803, March 2016.
  • M. Chen, et al, A novel approach to system design for dialect speech interaction with NAO robot, ICAR 2017:476-481.
  • Z. He, et al. Exploiting Real-Time Traffic Light Scheduling with Taxi Traces. ICPP 2016: 314-323.
  • Z. Tian, et al. Real-time charging station recommendation system for electric-vehicle taxis. IEEE Trans. on Intelligent Transportation Systems, 17(11): 3098-3109, Nov. 2016.
  • W. Xiong, et al. ShenZhen transportation system (SZTS): a novel big data benchmark suite. The Journal of Supercomputing 72(11): 4337-4364, Nov. 2016.

Teaching

Dr. Xu taught graduate and undergraduate courses related to computer architecture, computer networks, and distributed and parallel computing. He has created new courses about Scalable and Secure Internet Services and Machine Learning for the Design of Computer Systems. The machine learning course offered in Winter 2008 semester should be one of the first such in the world dedicated to the interactions between AI and systems.

Courses at Wayne State University:

  • ECE5650: Computer Networking and the Internet (W06, F06, W07, F07, F08, Fall 2009)
  • ECE7995: Machine Learning for Networked Computer Systems (Winter 2008)
  • ECE7650: Scalable Internet Services and Architecture (F00, F01, W03, W06, W09, Winter 2010)
  • ECE766: Advanced/Parallel Computer Architecture (F95,96,97,98, W99, F99, W00, F05)
  • ECE7610: Advanced Parallel and Distributed Systems (W96, W98, W02, W05, W07, W09, W13)
  • ECE562: Microprocessor and Embedded Systems
  • ECE561: Introduction to Parallel and Distribution Systems (F97, F01, F02, F04)
  • ECE468: Computer Organizations (W00, SS00, SS02)

show course list | hide

Professional Services

Dr. Xu serves (or served) in the editorial boards of a number of journals, including the leading journals in his field: IEEE Trans. on ComputersIEEE Trans. on Cloud Computing, IEEE Transactions on Parallel and Distributed Systems (2008-2012), Journal of Parallel and Distributed Computing, Science China: Information Science, and ZTE Communication. He also participated in the organization of several conferences and workshops in his field. He was the general chair of ACM/IEEE CCGrid’2015. He is also the current Chair of IEEE Technical Committee on Distributed Processing (2014-). As a National Expert of China, he also served in many ad hoc review panels from MOST, CAS, NSFC of China. 

PhD and Postdoctoral Positions Open

If you are self-motivated and hard-working, please send your CV to me, czxu@um.edu.mo.

PhD Fellowship
This PhD Fellowship scheme aims to recruit top talents from around the world to undertake PhD studies in the University of Macau. It is highly prestigious and only admits the very best in the world. If you have a proven record of academic excellence, research ability and potential, good communication and interpersonal skills, and leadership abilities, you are welcome to contact me to initiate the application procedure.

The UM Macao PhD Scholarship provides a monthly stipend of up to MOP20,000 (~US$2,500) for and a conference or research-related travel allowance up to MOP10,000 per academic year for a period up to 4 years. For awardees making excellent progress in research and who will need more time beyond the four-year period to finish their studies, I will continue the financial support with my research projects. More information can be found at https://grs.um.edu.mo/index.php/prospective-students/phd-funding/um-macao-phd-scholarship/.

Postdoctoral Fellowship
The fellowship aims to provide support to individual faculties to sustain and strengthen their research capabilities as well as to build up areas of expertise. It provides a monthly stipend of up to MOP40,000 (~US$5,000) for a contact of 2 years. See https://www.um.edu.mo/research/MPF%202019.html for more info about the fellowship. If your research background fits my research interests, please send your CV to me, indicating potential areas of collaboration.

Dr. Cheng-Zhong Xu
須成忠

Chair Professor, IEEE Fellow
Computer and Information Science
University of Macau

Dean
Faculty of Science and Technology

Interim Director
Institute of Collaborative Innovation