Professor Rui Zhang
- Artificial Intelligence (AI)
- Big Data Analytics
- Data Mining
- Data analysis and Data analytics
- Database systems
- Management of Data and Information
Dr Rui Zhang is a Professor and leader of the Big Data and Knowledge Research Theme at the School of Computing and Information Systems of the University of Melbourne. He is an internationally leading researcher in the area of big data, data mining and machine learning. Professor Zhang has won several awards including the prestigious Future Fellowship by the Australian Research Council in 2012, Chris Wallace Award for Outstanding Research by the Computing Research and Education Association of Australasia (CORE) in 2015, and Google Faculty Research Award in 2017. His inventions have been adopted by major IT companies such as AT&T and Microsoft. He proposed a novel technique for computing primitive statistics efficiently on extremely fast TCP/IP packet streams. He developed a temporal index called version compressed TSB-tree, which has been implemented in Microsoft’s flagship database product, Microsoft SQL Server. Dr Rui Zhang obtained his Bachelor's degree from Tsinghua University in 2001, PhD from National University of Singapore in 2006, and has then started as a faculty member in The University of Melbourne since 2007. Before joining the University of Melbourne, he has been a visiting research scientist at AT&T labs-research in New Jersey and at Microsoft Research in Redmond, Washington. Recently, he has been a visiting researcher at Microsoft Research Asia in Beijing regularly collaborating on his ARC Future Fellowship project. Dr Zhang's research interests include big data and AI, particularly in areas of recommendation systems, chatbot, knowledge bases, spatial and temporal data analytics, moving object management and data streams.
If you are looking for a PhD supervisor, please visit my personal website (http://www.ruizhang.info) before sending me an email.
- Wen, Z.; Deng, D.; Zhang, R.; Kotagiri, R. 2ED: An efficient entity extraction algorithm using two-level edit-distance. 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE. 2019, Vol. 2019-April, pp. 998-1009. DOI: 10.1109/ICDE.2019.00093
- Zhao, Y.; Qi, J.; Zhang, R. CBHE: Corner-based building height estimation for complex street scene images. The World Wide Web Conference on - WWW '19. ACM Press. 2019, pp. 2436-2447. DOI: 10.1145/3308558.3313394
- Wang, Z.; Zhang, R.; Qi, J.; Yuan, B. DBSVEC: Density-based clustering using support vector expansion. 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE. 2019, Vol. 2019-April, pp. 280-291. DOI: 10.1109/ICDE.2019.00033
- Jiang, W.; Qi, J.; Yu, JX.; Huang, J.; Zhang, R. HyperX: A Scalable Hypergraph Framework. IEEE Transactions on Knowledge and Data Engineering. IEEE COMPUTER SOC. 2019, Vol. 31, Issue 5, pp. 909-922. DOI: 10.1109/TKDE.2018.2848257
- Li, C.; Gu, Y.; Qi, J.; Zhang, R.; Yu, G. Moving kNN query processing in metric space based on influential sets. Information Systems. PERGAMON-ELSEVIER SCIENCE LTD. 2019, Vol. 83, pp. 126-144. DOI: 10.1016/j.is.2019.03.008
- Hashem, T.; Kulik, L.; Ramamohanarao, K.; Zhang, R.; Soma, SC. Protecting privacy for distance and rank based group nearest neighbor queries. World Wide Web. SPRINGER. 2019, Vol. 22, Issue 1, pp. 375-416. DOI: 10.1007/s11280-018-0570-5
- Zheng, T.; Zheng, X.; Zhang, Y.; Deng, Y.; Dong, E.; Zhang, R.; Liu, X. SmartVM: a SLA-aware microservice deployment framework. World Wide Web. SPRINGER. 2019, Vol. 22, Issue 1, pp. 275-293. DOI: 10.1007/s11280-018-0562-5
- Wang, X.; Qi, J.; Kotagiri, R.; Sun, Y.; Li, B.; Zhang, R. A joint optimization approach for personalized recommendation diversification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 10939 LNAI, pp. 597-609. DOI: 10.1007/978-3-319-93040-4_47
- Wang, S.; Bao, Z.; Huang, S.; Zhang, R. A unified processing paradigm for interactive location-based web search. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18. Association for Computing Machinery (ACM). 2018, Vol. 2018-Febuary, pp. 601-609. DOI: 10.1145/3159652.3159667
- Zhang, Y.; Dai, Y.; Qi, J.; Xu, X.; Zhang, R. Citation field learning by RNN with limited training data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2018, Vol. 11154 LNAI, pp. 219-232. DOI: 10.1007/978-3-030-04503-6_23
- Yin, C.; Zhang, R.; Qi, J.; Sun, Y.; Tan, T. Context-Uncertainty-Aware Chatbot Action Selection via Parameterized Auxiliary Reinforcement Learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). SPRINGER INTERNATIONAL PUBLISHING AG. 2018, Vol. 10937, pp. 500-512. DOI: 10.1007/978-3-319-93034-3_40
- Qi, J.; Kumar, V.; Zhang, R.; Tanin, E.; Trajcevski, G.; Scheuermann, P. Continuous maintenance of range sum heat maps. 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE. 2018, pp. 1625-1628. DOI: 10.1109/ICDE.2018.00192
- Qi, J.; Zhang, R.; Jensen, CS.; Ramamohanarao, K.; He, J. Continuous Spatial Query Processing: A Survey of Safe Region Based Techniques. ACM Computing Surveys. ASSOC COMPUTING MACHINERY. 2018, Vol. 51, Issue 3. DOI: 10.1145/3193835
- Trisedya, BD.; Qi, J.; Zhang, R.; Wang, W. GTR-LSTM: A triple encoder for sentence generation from RDF data. ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). 2018, Vol. 1, pp. 1627-1637.
- Li, M.; Bao, Z.; Sellis, T.; Yan, S.; Zhang, R. HomeSeeker: A visual analytics system of real estate data. Journal of Visual Languages & Computing. ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD. 2018, Vol. 45, pp. 1-16. DOI: 10.1016/j.jvlc.2018.02.001
- Wang, X.; Zhang, R.; Sun, Y.; Qi, J. KDGAN: Knowledge Distillation with Generative Adversarial Networks. Advances in Neural Information Processing Systems. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018, Vol. 31.
- Cheng, W.; Erfani, S.; Zhang, R.; Ramamohanarao, K. Learning datum-wise sampling frequency for energy-efficient human activity recognition. 32nd AAAI Conference on Artificial Intelligence, AAAI 2018. 2018, pp. 2143-2150.
- Gu, F.; Khoshelham, K.; Valaee, S.; Shang, J.; Zhang, R. Locomotion Activity Recognition Using Stacked Denoising Autoencoders. IEEE Internet of Things Journal. IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. 2018, Vol. 5, Issue 3, pp. 2085-2093. DOI: 10.1109/JIOT.2018.2823084
- Cheng, W.; Erfani, S.; Zhang, R.; Ramamohanarao, K. Predicting complex activities from ongoing multivariate time series. IJCAI International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence. 2018, Vol. 2018-July, pp. 3322-3328.
- Wen, Z.; Zhang, R.; Ramamohanarao, K.; Yang, L. Scalable and fast SVM regression using modern hardware. World Wide Web. SPRINGER. 2018, Vol. 21, Issue 2, pp. 261-287. DOI: 10.1007/s11280-017-0445-1
View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile