General

Qu Qiang is a full professor at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. He won the the special research position of the Chinese Academy of Sciences, and the title of Shenzhen Local Leading Talent. He is currently the director of Guangdong Provincial Blockchain and Distributed IOT Security Engineering Research Center, the deputy director of Shenzhen High Performance Data Mining Key Laboratory, and th director/the Chief Scientist of Huawei Cloud Blockchain Laboratory. He is a member of the China Computer Federation Database Special Committee, China Computer Federation Blockchain Special Committee, and China Federation of Logistics and Purchasing Blockchain Application Branch Expert Committee . In addition, Qu Qiang is a visiting professor at Innopolis University, and has worked at Carnegie Mellon University, ETH Zurich and Singapore Management University. His research interests are in blockchain, database and data intelligence systems. His 100+ papers have been published by top academic journals and conferences, and he has written several monographs and books both in Chinese and English, including "Blockchain + AI: Unveiling the economical future".  The spatial-temporal blockchain technology won the best paper award by IBM and WISE 2018 in Dubai, etc. Dr. Qu joined the Chinese Academy of Sciences at the end of 2016. In 2017, he was exceptionally promoted to a doctoral supervisor, and in 2020, he was exceptionally promoted to a full professor. He has been a principle investigtor (PI) for a number of international and national projects, and he is now the chief scientist for a project supported by National key research and development program of China.  


Experience


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

1. COURSES FOR 2015-2016 FALL SEMESTER AT INNOPOLIS UNIVERSITY
DATA MODELLING AND DATABASES (6 ECTS)

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.

For more details..

DATA SCIENCE PROJECT MODULE 1 (10 ECTS)

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.

INFORMATION RETRIEVAL (6 ECTS)

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.

2. COURSES FOR 2014-2015 SPRING SEMESTER
MACHINE LEARNING (6 ECTS)

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.

INFORMATION RETRIEVAL (6 ECTS)

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 (6 ECTS)

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.

SOCIAL NETWORK ANALYSIS (6 ECTS)

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.


Publications

   
Papers

1. S. Zhang, S. M. H. Bamakan, Qiang Qu and S. Li, "Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective," in IEEE Reviews in Biomedical Engineering, vol. 12, pp. 194-208, 2019 (Corresponding,JCR Q1,2023 IF 17.6)

2. Su Yao, Mu Wang, Qiang Qu, Ziyi Zhang, Yi-Feng Zhang, Ke Xu, Mingwei Xu: Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing. IEEE J. Sel. Areas Commun. 40(12): 3485-3500 (2022)  (JCR Q1,2023 IF 16.4)

3. Seyed Mojtaba Hosseini Bamakan, Ildar Nurgaliev, Qiang Qu: Opinion leader detection: A methodological review. Expert Systems with Applications 115: 200-222 (2019) (Corresponding,JCR Q1,2023 IF 8.5)

4. Muhammad Muzammal, Qiang Qu,Bulat Nasrulin:Renovating blockchain with distributed databases: An open source system. Future Generation Computer Systems, 90:105-117 (2019).  (Corresponding,JCR Q1,2023 IF 7.5)

5. Qiang Qu, Ildar Nurgaliev, Muhammad Muzamma:On Spatio-temporal Blockchain Query Processing. Future Generation Computer Systems, 2019. accepted  (JCR Q1,2023 IF 7.5)

6. Min Yang, Wenpeng Yin, Qiang Qu, Wenting Tu, Ying Shen, Xiaojun Chen: Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classification. IEEE Trans. Affect. Comput. 12(3): 761-775 (2021)   (JCR Q1,2023 IF 11.2)

7. Jiehuan Luo, Xin Cao, Xike Xie, Qiang Qu, Zhiqiang Liu, Christian S. Jensen. Efficient Attribute-Constrained Co-Located Community Search, ICDE 2020: 1201-1212 (Corresponding,CCF A)

8. Ali Hadian, Sadegh Nobari, Behrouz Minaei-Bidgoli, Qiang Qu: ROLL: Fast In-Memory Generation of Gigantic Scale-free Networks. SIGMOD Conference 2016: 1829-1842 (CCF A

9. Feida Zhu, Qiang Qu, David Lo, Xifeng Yan, Jiawei Han, Philip S. Yu: Mining Top-K Large Structural Patterns in a Massive Network. Proc. VLDB Endow. 4(11): 807-818 (2011)  (CCF A

10. 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)   (JCR Q1,2023 IF 8.9)