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, and interested in industry-driven data analytics research that makes real-world impact. 

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


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

Here is my Google Scholar page

  • 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, Capital Market, 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, 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.

      Current Student Collaboration / Supervision:

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

      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):