Wray Buntine, PhD

Wray Buntine, PhD

Director, Computer Science Program

College of Engineering & Computer Science

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ORCID: click here


Prior to his current appointment at VinUniversity, Wray Buntine was a full professor, foundation director of the Master of Data Science, and director of the Machine Learning Group at Monash University. Previously, he was conducting research projects at Helsinki Institute for Information Technology, NASA Ames Research Center, University of California, Berkeley, and Google. In the ’90s he was involved in a number of startups for both Silicon Valley and Wall Street. Professor Wray is known for his theoretical and applied work and in probabilistic methods for document and text analysis, social networks, data mining and machine learning. He is on several journal editorial boards and has been a senior program committee member for premier conferences such as IJCAI, UAI, AAAI, EMNLP, ICLR, ACML and NeurIPS. He has over 200 academic publications, several software products and two patents.


Machine Learning, Deep Learning, Data Mining, Natural Language Processing and Data Science.



  1. Buntine, “Machine learning after the deep learning revolution,” Frontiers of Computer Science 14(6) 2020,
  2. Doogan, W. Buntine, H. Linger & S. Brunt, “Public perceptions and attitudes toward COVID-19 nonpharmaceutical interventions across six countries: a topic modeling analysis of Twitter data,” Journal of Medical Internet Research, 22(9), 2020.
  3. Zhao, L. Du, W. Buntine & G. Liu, “Leveraging external information in topic modelling,” Knowledge and Information Systems, 61(2), 2020, pp. 661-693.
  4. Petitjean, W. Buntine, G.I. Webb & N. Zaidi, “Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes,” Machine Learning, 107(8-10), 2018, pp. 1303-1331.
  5. W. Lim, W. Buntine, C. Chen & L. Du, “Nonparametric Bayesian topic modelling with the hierarchical Pitman–Yor processes,” International Journal of Approximate Reasoning, 78, 2016, pp. 172-191
  6. Chen, W. Buntine, N. Ding, L. Xie & L. Du, “Differential topic models,” IEEE Transactions on Pattern Analysis and Machine Intelligence. 37(2), 2015, p. 230-242.
  7. Tuytelaars, C.H. Lampert, M.B. Blaschko & W.L. Buntine, “Unsupervised object discovery: a comparison,” International Journal of Computer Vision. 88(2), 2010, pp. 284-302.
  8. Jakulin, W.L. Buntine, T.M. LaPira & H. Brasher, “Analyzing the U.S. Senate in 2003: similarities, clusters, and blocs,” Political Analysis, 17, 2009, pp. 291-310.
  9. Buntine, “A guide to the literature on learning probabilistic networks from data,” IEEE Transactions on knowledge and data engineering 8(2), 1996, 195-210.
  10. L. Buntine & A.S. Weigend, “Computing Second Derivatives in Feedforward Networks: A Review,” IEEE Transactions on Neural Networks, 5(3), 1994, pp. 480-488.
  11. L. Buntine, “Operations for learning with graphical models,” Journal of Artificial Intelligence Research 2, 1994, pp. 159-225.
  12. Buntine, “Learning classification trees,” Statistics and Computing, 2(2), 1992, pp. 63-73.


  1. Zaremoodi, W. Buntine & G. Haffari, “Adaptive knowledge sharing in multi-task learning: improving low-resource neural machine translation,” ACL 2018 – The 56th Annual Meeting of the Association for Computational Linguistics: Proceedings of the Conference, Vol. 2 p. 656-661.
  2. Liu, W. Buntine & G. Haffari, “Learning to actively learn neural machine translation,” CoNLL 2018 – The 22nd Conf. on Computational Natural Language Learning – Proceedings of the Conference, 2018, pp. 334-344.
  3. W. Lim & W.L. Buntine, “Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon,” Proc. of the 23rd ACM Int. Conf. on Information and Knowledge Management, 2014, pp. 1319-1328.
  4. L. Buntine & S. Mishra, “Experiments with non-parametric topic models,” KDD’14: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014, pp. 881-890.
  5. W. Lim & W.L. Buntine, “Twitter opinion topic model: Extracting product opinions from tweets by leveraging hashtags and sentiment lexicon,” Proc. of the 23rd ACM Int. Conf. on Information and Knowledge Management, 2014, pp. 1319-1328.
  6. Mehrotra, S.P. Sanner, W.L. Buntine & L. Xie, “Improving LDA topic models for microblogs via tweet pooling and automatic labeling”  Proc. of the 36th Int. ACM SIGIR Conference on Research & Development in Information Retrieval, 2013 pp. 889-892.
  7. Du, W.L. Buntine & M. Johnson, Topic Segmentation with a Structured Topic Model,” Proc. of NAACL-HLT 2013, pp. 190-200.
  8. Newman, E.V. Bonilla & W.L. Buntine, “Improving topic coherence with regularized topic models,” Advances in Neural Information Processing Systems 24, 2011, p. 1-9
  9. Buntine, “Theory refinement on Bayesian networks,”  Uncertainty in Artificial Intelligence Proceedings 1991, pp. 52-60.


  1. JJ Oliver, WL Buntine, G Roumeliotis, “System and method for adaptive text recommendation,” US Patent 6,845,374, 2005.
  2. J Oliver, R Baxter, W Buntine, S Waterhouse, “Method and system providing user with personalized recommendations by electronic-mail based upon the determined interests of the user pertain to the theme and concepts of the ,” US Patent 7,158,986, 2007.

For the full list of publications please visit this link.


  • 1st place for 1st year u/grad in Sydney Uni. Mathematical Society Problems Competition, 1977.
  • Priest Memorial Prize (2nd year science), University of Queensland, 1978.
  • Artificial Intelligence Best Paper Award at ECAI-86.
  • NASA Certificate of Recognition for creative development of software (IND2.1), 2013.


  • 2006: Title of Docent, Helsinki University, Helsinki, Finland
  • 1985-1992: Doctor of Philosophy in Computer Sciences, University of Technology, Sydney, Sydney, Australia.
  • 1983-1984: Master of Science Qualifying in Computer Science (1st class honours), University of Queensland, Brisbane, Australia.
  • 1977-1977: Bachelor of Science, University of Queensland, Brisbane, Australia.