Wei Liu (刘伟)


Phone: +61 2 9514 3782

Email: wei.liu[at]uts.edu.au

Address: Room 11.07.307, Level 7, Building 11 (the Broadway building)PO Box 123, Broadway NSW 2007, AUSTRALIA

Short Biography:

I'm a Research Program Leader and a Lecturer of Data Science at the Advanced Analytics Institute, Faculty of Engineering and IT, in the University of Technology Sydney. 

Before joining UTS, I was a Data Mining Research Fellow at the University of Melbourne working with Prof Rao Kotagiri, Prof James Bailey, and Prof Chris Leckie, and then an Industry-focused Machine Learning Researcher and Project Manager at NICTA working with the transportation industry. I was educated at the University of Sydney where I obtained my PhD degree in Prof Sanjay Chawla's research group. 

I'm intensively engaged in Business and Industry R&D practices at UTS, and I'm interested in industry-driven data science research that makes real-world impact. 

In theoretical terms, I have been publishing papers in research areas of: graph mining, causal inference, tensor factorization, multimodal feature extraction, game theory, data imbalance learning, spatio-temporal data mining, and anomaly/outlier detection.


Publications (In a nutshell, I have published over 25 top tier A/A* ranking papers, and I'm the first author for 11 of them. Besides, my publications have won 3 best paper awards.)

Here is my Google Scholar page

  • Hoang Nguyen, Wei Liu, and Fang Chen. Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data. In IEEE Transactions on Big Data (Invited).
  • Ling Luo, Wei Liu, Irena Koprinska, and Fang Chen. Discovering Causal Structures from Time Series Data via Enhanced Granger Causality. In Proceedings of the 28th Australasian Joint Conference on Artificial Intelligence 2015 (AI 2015).
  • Hoang Nguyen, Wei Liu, and Fang Chen. Discovering Congestion Propagation Patterns in Spatio-Temporal Traffic Data. In Proceedings of the 4th International Workshop on Urban Computing (UrbComp 2015) in conjunction with the 21st ACM SIGKDD (KDD 2015) conference.
  • Jingyu Shao, Junfu Yin, Wei Liu, and Longbing Cao. Mining Actionable Combined Patterns Satisfying both Utility and Frequency Criteria. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2015).
  • Qianqian Chen, Liang Hu, Jia Xu, Wei Liu, and Longbing Cao. Document Similarity Analysis via Involving both Explicit and Implicit Sematic Relatedness. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2015).
  • Ling Luo, Wei Liu, Irena Koprinska, and Fang Chen. Discrimination-Aware Association Rule Mining for Unbiased Data Analytics. In Proceedings of the 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015).
  • Ling Luo, Irena Koprinska and Wei Liu. Discrimination-Aware Classifiers for Student Performance Prediction. In Proceedings of the 8th International Conference on Educational Data Mining (EDM 2015).
  • Khoa Nguyen, Wei Liu, Fang Chen, Peter Runcie, et al.: Using Tensor Analysis for Damage Identification in Civil Structures. In proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015). Accepted as a Long Presentation -- acceptance rate 7%
  • Xinxin Jiang, Wei Liu, Longbing Cao, Guodong Long: Coupled Collaborative Filtering for Context-aware Recommendation. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015). 
  • Jingyu Shao, Junfu Yin, Wei Liu, Longbing Cao: Actionable Combined High Utility Itemset Mining. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI 2015). 
  • Fei Wang, Wei Liu, Sanjay Chawla: On Sparse Feature Attacks in Adversarial Learning. In proceedings of the 2014 IEEE International Conference on Data Mining (ICDM 2014). (Acceptance rate: 19%)
  • Wei Liu, Dong Lee, and Rao Kotagiri: Significantly Improving General Supervised Learners by Using Local Information.  In proceedings of the 23rd ACM Conference on Information and Knowledge Management (CIKM 2014). (Acceptance rate: 17%)
  • Victor Chu, Wei Liu, Raymond Wong, Fang Chen, and Charles Perng: Traffic Analysis as a Service via a Unified Model. In proceedings of the 11th IEEE International Conference on Services Computing (IEEE SCC 2014). 
  • Victor Chu, Wei Liu, Raymond Wong, and Fang Chen: Causal Structure Discovery for Spatio-temporal Data. In proceedings of the 19th International Conference on Database Systems for Advanced Applications (DASFAA 2014). 
  • Jeffrey Chan, Wei Liu, James Bailey, Christopher Leckie, and Rao Kotagiri: Structure-aware Distance Measures for Comparing Clusterings in Graphs. In proceedings of the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014). 
  • Wei Liu, James Bailey, Christopher Leckie, Fang Chen, and Rao Kotagiri: A Bayesian Classifier for Learning from Tensorial Data. In proceedings of the 2013 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2013). 
    • Wei Liu, Andrey Kan, James Bailey, Christopher Leckie, Jian Pei and Rao Kotagiri: On Compressing Weighted Time-evolving Graphs. In proceedings of the 21st ACM Conference on Information and Knowledge Management (CIKM 2012). 
    • Linsey Pang, Sanjay Chawla, Wei Liu and Yu Zheng: On Detection of Emerging Anomalous Traffic Patterns Using GPS Data. Data & Knowledge Engineering journal, vol. 87, pages 357-373, 2013. 
      • Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu and Yu Zheng: On Mining Emerging Patterns on Traffic Networks. In Proceedings of the 7th International Conference on Advanced Data Mining and Applications (ADMA 2011). This paper won the best paper award. 

      Industry Engagement

      I have led a number of significant research projects in partnership with government agencies and industrial organisations, spanning Internet Security, Insurance, Finance, Transportation, Infrastructure, Policy and Regulation sectors. I developed cutting-edge data mining methods and software tools for the transport industry, which accurately identify causes of road incidents from dynamic traffic networks. I also designed advanced award-winning predictive analysis models for the problems of rare event prediction, fraud/intrusion detection, emerging trends detection, and spam filtering with time-evolving data distributions, which are validated and applied in different industrial organisations. Details of some of my research projects are in the below:
      • "Analytics Model to Support Strategic Planning in a Regulatory Environment", industry partner: NSW Department of Fair Trading; April - July 2015.
      • "Transport Data Science and Advanced Analytics", industry partner: National ICT Australia; July 2015 – June 2017.
      • “Traffic Watch for Transport Control Service”, industry partner: Transport Management Centre; May 2013 – June 2014.
      • “Congestion Propagation and Hotspot Detection in Sydney CBD”, industry partner: NSW RMS; Aug – Dec 2013.
      • “Data Fusion Technologies for Comprehensive Transport Data Analysis in Melbourne”, industry partner: VicRoads; Jun – Sep 2013.
      • “Time of Arrival Estimations using HD Vehicle Trajectories”, industry partner: Tomtom. Jan 2013 – March 2013.
      • “Early Detection of Road Traffic Incidents using Social Media”, industry partner: the Transport Management Centre; Oct – Dec 2012.
      • “Causal Inference for Sequential Traffic Congestion", industry partner: Microsoft Research Asia; Nov 2010 – Mar 2011.
      • “Abnormal Claim Detection from Worker’s Compensations”, industry partner: CGU Insurance; Mar 2010 – Jun 2011.
      • “Data Integration for Cross-Market Capital Trading Systems”, industry partner: the SMARST Group (now purchased by Nasdaq), Jun 2008 – Dec 2009.

      Student Collaboration / Supervision:

      • Quan Do, PhD student, Tensor-based data fusion analysis
      • Ling Luo, PhD student, on causal inference for spatio-temporal data   
      • Xinxin Jiang, PhD student, on context-aware recommendation systems
      • Fei Wang, PhD student, on game theory for machine learning  
      • Shoujin Wang, PhD student, on non-i.i.d. pattern mining 

      Completed Student Supervision / Collaboration

      • Victor Chu, PhD student, causal structure discovery from road traffic data  
      • Goce Ristanoski, PhD student, on time series regression (with Prof James Bailey) 
      • Mohammad Mazraehshahi, Master student, on soft-cut decision trees (with Prof Rao Kotagiri)
      • DongHawn Lee, Master student, on lazy learning methods (with Prof Rao Kotagiri) 
      • Jinheng Liu, Master student, ensembles for outlier detection
      • Emma Wang, Master student, feature creation for Bagging 
      • Chun Yuan Lee, Summer intern student, Causality Models for Understanding Road Traffic Changes
      • Amogh Sarda, Summer intern student, Incident impact analysis by earning historical road traffic data 
      • Han Zhou, Summer intern student, Mining social networks for early detection of events  
      • Zeyang Yu, Summer intern student, on Twitter-based road incident detection. 
      • Mingxuan Li, Summer intern student (winner of NICTA summer scholar prize), on mining dynamic road traffics.


      My Readings/Focus (some via library login)

        A* / A ranking venues (rankings are from arc.gov.au):