Qiang Qu is a professor and the director of Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security. Before joining SIAT, he was an assistant professor, the director of Dainfos Lab, and 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 research fellow at Singapore Management University. He obtained his M.Sc in Computer Science from Peking University. His current research interests are in data-intensive applications and systems, focusing on efficient and scalable algorithm design, blockchain, data sense-making, and mobility intelligence. He was TPC member of several prestige conferences, and he chaired workshops in VLDB 2018, ICDM 2018, VLDB 2017, ICDM 2015, and APWEB-WAIM 2017 on mobility analysis.


Work Experience

  • SIAT  Global Center for Big Mobile Intelligence,Executive Director | 2016.10-Until Now

  • Innopolis University  Institute of Information Systems,Assistant Professor | 2015.2-2016.12

  • ETH Zurich  Computer Science,Visiting Scientist | 2014.10-2015.1

  • Carnegie Mellon University  Computer Science,Visiting Scholar | 2013.11-2014.4

  • Singapore Management University  Information Systems,Visiting Scholar | 2013.7-2013.10

  • Nanyang Technological University  Computer Science,Visiting Scholar | 2012.5-2012.7

  • Singapore Management University  Information Systems,Research Fellow | 2009.12-2010.12

Teaching Experience


The course presents an introduction to data modelling & database management systems (DBMSs). Most commercial applications involve the use of a database management system to store information. A bank ATM has access to customer balance stored in a database. When you use a credit card, information about your card and each transaction is stored in a database. The state Department of Motor Vehicles keeps track of your drivers license and your car in databases. This course will cover the design of database systems, important database theories, SQL, programming and relational databases, and logical, object -oriented, and XML databases. The course will also involve projects and exercises using PostgreSQL, an SQL database, and the web.

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This course is designed for data science master students. Data Science Project Module I exposes the most recent topics in Data Science especially in very large scale data management, data mining, and data analysis to students. The topics will be selected from recent conferences and journals, building up interest and basics for students on a wide range of data science topics. The output is that each student at the end of the semester is able to understand the basics and the state-of-the-art results of a concrete data science topic, presenting scientifically comparable analysis.


The course presents an introduction to the field of information retrieval and discusses automated techniques to effectively handle and manage unstructured and semi-structured information. This includes methods and principles that are at the heart of various systems for information access, such as Web or enterprise search engines, categorization and recommended systems, as well as information extraction and knowledge management tools.


This course review the necessary statistical preliminaries and provide an overview of commonly used machine learning methods. Our focus is on real understanding of supervised learning, unsupervised, learning theory, and reinforcement learning and adaptive control. The course will also cover recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.


The course presents an introduction to the field of information retrieval and discusses automated techniques to effectively handle and manage unstructured and semi-structured information. This includes methods and principles that are at the heart of various systems for information access, such as Web or enterprise search engines, categorization and recommended systems, as well as information extraction and knowledge management tools.


Data Mining is an analytic process, which explores large data sets (also known as big data) to discover consistent patterns. This computational process involves a use of methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. This course will discuss such algorithms for analyzing large amount of data sets. The topics include: Clustering, Link Analysis, Mining the Web Structured Data, Text Mining, etc.


A social network is a social structure made up of a set of social actors and a set of the dyadic ties between these actors. Social network analysis studies the relationships of the nodes (actors) in the network, represented by edges (ties). The course will cover theoretical aspects of the structure as well as practical application of popular social networks. In this course, you will learn various topics including diffusion of information, community detection, and visualization. You will have a hands-on experience of acquiring social network data and analyzing it with different algorithms.



  • Muhammad Muzammal, Qiang Qu*, Bulat Nasrulin:Renovating blockchain with distributed databases: An open source system. Future Generation Computer Systems, 2018. (JCR Q1, IF=4.639)
  • Seyed Mojtaba Hosseini Bamakan, Ildar Nurgaliev, Qiang Qu*: Opinion Leader Detection: A Methodological Review. Expert system with applications, 2018. (JCR Q1, IF=3.768)
  • Bulat Nasrulin, Muhammad Muzammal, Qiang Qu*: ChainMOB: Mobility Analytics on Blockchain. MDM 2018: 292-293
  • Siyuan Liu, Qiang Qu*, Shuhui Wang. Heterogeneous anomaly detection in social diffusion with discriminative feature discovery. Information Sciences, 2018, 439: 1-18 (JCR Q1, IF=4.832)
  • Min Yang, Qiang Qu*, Kai Lei, Jia Zhu, Zhou Zhao, Xiaojun Chen, Joshua Zhexue Huang: Investigating Deep Reinforcement Learning Techniques in Personalized Dialogue Generation. SDM 2018: 630-638. (CCF B)
  • Min Yang, Wenting Tu, Qiang Qu, Xiaojun Chen, Zhou Zhao, Jia Zhu. Personalized Response Generation by Dual-learning based Domain Adaptation. Neural Networks, 103: 72-82 (2018) (JCR Q1, IF=7.197)
  • Min Yang, Qiang Qu, Xiaojun Chen, Chaoxue Guo, Ying Shen, Kai Lei: Feature-enhanced attention network for target-dependent sentiment classification. Neurocomputing 307: 91-97 (2018) (JCR Q1, IF=3.317)
  • Juanjuan Zhao, Qiang Qu*, Fan Zhang, Chengzhong Xu, Siyuan Liu. Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data. IEEE Transactions on Intelligent Transportation Systems (IEEE TITS), pp(99): 1-12 (2017) (JCR Q1, IF=3.729)
  • Sadegh Nobari, Qiang Qu*, Christian S. Jensen. In-memory Spatial Join: Data Matters! EDBT 2017: 462-465 (CCF B)
  • Qiang Qu*, Siyuan Liu, Feida Zhu, Christian S. Jensen. Efficient Online Summarization of Large-scale Dynamic Networks. IEEE Trans. Knowl. Data Eng. 28(12): 3231-3245 (2016) (CCF A; JCR Q1, IF=3.438)
  • Siyuan Liu, Qiang Qu*. Dynamic Collective Routing using Crowdsourcing Data. Transportation Research Part B Methodology, 2016, 93: 450-469. (JCR Q1, IF=3.769)
  • Fang Zhou, Qiang Qu, Hannu Toivonen. Summarization of Weighted Networks, Journal of Experimental & Theoretical Artificial Intelligence, 2016 (JCR Q2, IF=1.384)
  • Ali Hadian, Sadegh Nobari, Behrouz Minaei-Bidgoli, Qiang Qu. ROLL: Fast In-Memory Generation of Gigantic Scale-free Networks. SIGMOD, 2016: 1829-1842 (CCF A)
  • Mingli Zhang, Christian Desrosiers, Qiang Qu*, Fenghua Guo, Caiming Zhang. Medical Image Super-resolution with Non-local Embedding Sparse Representation and Improved IBP. ICASSP, 2016 (CCF B)
  • Qiang Qu*, Cen Chen, Christian S. Jensen, Anders Skovsgaard. Space-Time Aware Behavioral-Topic Modeling for Microblog Posts. IEEE Data Eng. Bull. 38(2): 58-67 (2015)
  • Siyuan Liu, Qiang Qu, Shuhui Wang. Rationality Analytics from Trajectories. ACM Transactions on Knowledge Discovery from Data 10(1): 10 (2015) (CCF B; JCR Q3, IF=1.895)
  • Siyuan Liu, Qiang Qu*, Ce Liu, Lei Chen, Lionel M. Ni. SMC: Privacy-preserved Data Sharing over Distributed Data Streams. IEEE Transactions on Big Data, 2015, 1(2):68-81.
  • Qiang Qu*, Siyuan Liu, Christian S. Jensen, Feida Zhu, Christos Faloutsos. Interestingness-driven Diffusion Process Summarizing in Dynamic Networks. ECML/PKDD 2014: 597–613 (CCF B)
  • Qiang Qu*, Siyuan Liu, Bin Yang, Christian S. Jensen. Efficient Top-k Spatial Locality Search for Co-located Spatial Web Objects. MDM 2014: 269–278
  • Qiang Qu*, Bin Yang, Christian S. Jensen. Integrating Non-spatial Preferences with Spatial Location Queries. SSDBM 2014: 8:1–8:12
  • Feida Zhu, Zequn Zhang, Qiang Qu. A Direct Approach to Efficient Constrained Graph Pattern Discovery. SIGMOD 2013: 821–832 (CCF A)
  • Xin Cao, Lisi Chen, Gao Cong, Christian S. Jensen, Qiang Qu, Anders Skovsgaard, Dingming Wu, Man Lung Yiu: Spatial Keyword Querying. ER 2012: 1–14 (Invited Paper)
  • Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu: Mining Top-K Large Structural Patterns in a Massive Network. PVLDB 4(11): 807–818 (2011) (CCF A)
  • Qiang Qu*, Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu, Hongyan Li: Efficient Topological OLAP on Information Networks. DASFAA 2011: 389–403 (CCF B)
  • Coauthor of Book Chapter in Encyclopedia of GIS, Edition 2 published by Springer.
  • Christos Doulkeridis, George A. Vouros, Qiang Qu, Shuhui Wang: Mobility Analytics for Spatio-Temporal and Social Data – First International Workshop, MATES 2017, Munich, Germany, September 1, 2017, Revised Selected Papers. Lecture Notes in Computer Science 10731, Springer 2018, ISBN 978-3-319-73520-7.