Personal profile

Biography

Wray is a Professor in the Faculty of Information Technology at Monash University in Melbourne, Australia.

He enjoys playing with web/text data and writing code, and supports some of his work on Github (wbuntine). His research interests include theoretical and applied work in document and text analysis, machine learning, and probabilistic methods including:

  • discrete non-parametric Bayesian statistics
  • latent variables in semi-structured and text analysis
  • topic models for semi-structured data

He teaches in 2018 Introduction to data science (FIT5145) and Modelling for data analysis (FIT5197) and in 2019 Data analysis for semi-structured data (FIT5212).

See more at blog and academic webpage at TopicModels.ORG website (see links section, below).

Find me on GoogleDBLPORCID and ResearchGate.

Current activity:

  • attended 9th ACML in Seoul, Korea in November, 2017
  • attended ICML and UAI in Sydney, August 2017
  • focused on launch of our data science postgraduate programmes, GDDS and MDS, 2015 and early 2016
  • ANU PhD student Kar Wai Lim attended CIKM 2014 in Shanghai early November to present our paper "Twitter Opinion Topic Model: Extracting Product Opinions from Tweets by leveraging Hashtags and Sentiment Lexicon" 

Students:

  • Novi Quadrianto: ANU. Supervisory panel member for Doctoral Thesis "Learning for the Internet:
    Kernel Embeddings and Optimisation," awarded 2011.
  • Lan Du: ANU. Supervisor for Doctoral Thesis "Non-parametric Bayesian Methods for Structured Topic Models"awarded 2012. 
  • Changyou Chen: ANU. Supervisor for PhD Thesis "Dependent Normalized Random Measures," from 2011, awarded 2014.
  • Aaron Li: ANU. Supervisor for Honours Thesis "Multi-GPU Distributed Parallel Bayesian Differ-
    ential Topic Modelling," awarded 1st class honours December 2012. 
  • Kar Wai Lim: ANU. Supervisor for PhD Thesis, started 2012, completed 2016.
  • Swapnil Mishra: Supervisor for Master Thesis "Sparse and Hierarchical Topic Modeling." Awarded 1st class honours, July 2014.

Related Links:

Research interests

Theoretical and applied research in document and text analysis, data mining and machine learning, probabilistic methods and information retireval. Main effort is in applying probabilistic and non-parametric Bayesan methods to tasks such as text analysis, topic modelling and language analysis.

External positions

Fellow, Helsinki Institute for Information Technology

4 Apr 2007 → …

Keywords

  • Artificial Intelligence
  • Bayesian non-parametrics
  • Computational intelligence and neural networks
  • Data Analysis
  • Data mining
  • Text mining

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2013 2016

Research Output 2008 2017

Efficient parameter learning of Bayesian network classifiers

Zaidi, N. A., Webb, G. I., Carman, M., Petitjean, F., Buntine, W., Hynes, M. & De Sterck, H. 26 Jan 2017 (Accepted/In press) In : Machine Learning. p. 1-41 41 p.

Research output: Research - peer-reviewArticle

A fusion of predicate logic and document semantic distance method orientated on data and context mapping

Liu, G., Sun, S., Buntine, W. & Yang, X. 13 Jun 2016 2015 4th International Conference on Computer Science and Network Technology (ICCSNT 2015). Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, p. 645-651 7 p. 7490828

Research output: ResearchConference Paper

An association rules text mining algorithm fusion with K-means improvement

Liu, G., Fu, W., Buntine, W. & Du, Y. 13 Jun 2016 2015 4th International Conference on Computer Science and Network Technology (ICCSNT 2015). Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, p. 781-785 5 p. 7490858

Research output: ResearchConference Paper

Bibliographic analysis on research publications using authors, categorical labels and the citation network

Lim, K. W. & Buntine, W. 1 May 2016 In : Machine Learning. 103, 2, p. 185-213 29 p.

Research output: Research - peer-reviewArticle

Nonparametric Bayesian topic modelling with the hierarchical Pitman–Yor processes

Lim, K. W., Buntine, W., Chen, C. & Du, L. 1 Nov 2016 In : International Journal of Approximate Reasoning. 78, p. 172-191 20 p.

Research output: Research - peer-reviewArticle

Activities 2016 2016

  • 3 Committees and working groups