Qiang Qu, Ph.D

Qiang Qu is an associate professor, the executive director of Global Center for Big Mobile intelligence at Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). Before joining SIAT, he was an assistant professor, the director of Dainfos Lab, the Associate Head for Research at Institute of Information Systems of Innopolis University.

He received his Ph.D. at Aarhus University, supervised by Prof. Christian S. Jensen. His Ph.D. research was supported by the GEOCrowd project under Marie Skłodowska-Curie Actions. He was a visiting scientist working with Prof. Gustavo Alonso at ETH Zurich in 2014-2015, a visiting scholar working with Prof. Christos Faloutsos at Carnegie Mellon University, a visiting scholar working with Prof. Gao Cong at Nanyang Technological University, and a visiting scholar working with Prof. Feida Zhu at Singapore Management University. He obtained his M.Sc in Computer Science from Peking University. His current research interests are in smart transportation, spatial-rich data management, large-scale data mining, knowledge-base systems, and information flow and cascading behavior in networks. Along his study, he has been awarded, e.g., Canon Scholarship, China Excellence National Scholarship, and Honorable Awards of International Mathematics Modeling Competition and Java Cup Competition. He has work experience in Sun Microsystems and IBM China Research Lab, and he has been involved in several projects supported by governments and companies.

Teaching

Prof. Qiang Qu is teaching the following courses in 2015.

  • Data Modelling and Databases
  • Data Science Project Model I
  • Information Retrieval
  • Machine Learning

Course materials can be found at university Internal Website. Office hours: every Monday.

Research

Prof. Qiang Qu is particularly interested in research topics including spatial data management, large-scale data mining, social network analysis, and sensor networks. Recently, his research also touches upon cloud computing, privacy issues, and machine learning.

Publications

A list of published papers can be found at DBLP .