Wray Buntine

Wray Buntine, PhD

Director, Computer Science program, College of Engineering and Computer Science


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. More recently he has been heavily involved in medical informatics. He will be general chair for Asian Conference on Machine Learning in Hanoi 2024. He is also co-Editor-in-Chief for the new ACM Transactions on Probabilistic Machine Learning and on several other 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 18 book chapters, 48 journal articles and 81 refereed conference papers, several software products and two patents, with over 13,000 citations and a Google h-index of 49.

• Machine Learning and Deep Learning, especially from a Bayesian perspective
• Data Science
• Natural Language Processing, especially in low resource scenarios
• Medical Informatics

1. W Tan, L Du, W Buntine, “Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, to appear 2024.
2. Lin, Jionghao, Tan, Wei, Du, Lan, Buntine, Wray, Lang, David, Gašević, Dragan & Chen, Guanliang 2024, ‘Enhancing Educational Dialogue Act Classification With Discourse Context and Sample Informativeness’, IEEE Transactions on Learning Technologies, vol. 17, pp. 258–269.

3. Caitlin Doogan, Buntine, Wray & Linger, Henry 2023, ‘A systematic review of the use of topic models for short text social media analysis’, Artif. Intell. Rev., vol. 56, no. 12, pp. 14223–14255, 2023
4. LV Jospin, H Laga, F Boussaid, W Buntine, M Bennamoun, “Hands-on Bayesian neural networks—A tutorial for deep learning users”, IEEE Computational Intelligence Magazine 17 (2), 29-48, 2022.
5. Buntine, Wray, ‘Understanding Hierarchical Processes’, Entropy, vol. 24, no. 12, pp. 1703, 2022
6. W Buntine, “Machine learning after the deep learning revolution,” Frontiers of Computer Science 14(6) 2020,
7. C 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.
8. F Petitjean, W Buntine, GI Webb & N Zaidi, “Accurate parameter estimation for Bayesian network classifiers using hierarchical Dirichlet processes,” Machine Learning, 107(8-10), 2018, pp. 1303-1331.
9. 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
10. C 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.
11. T Tuytelaars, CH Lampert, MB Blaschko & WL Buntine, “Unsupervised object discovery: a comparison,” International Journal of Computer Vision, 88(2), 2010, pp. 284-302.
12. A Jakulin, WL Buntine, TM LaPira & H Brasher, “Analyzing the U.S. Senate in 2003: similarities, clusters, and blocs,” Political Analysis, 17, 2009, pp. 291-310.
13. W Buntine, “A guide to the literature on learning probabilistic networks from data,” IEEE Transactions on Knowledge and Data Engineering, 8(2), 1996, 195-210.
14. WL Buntine, “Operations for learning with graphical models,” Journal of Artificial Intelligence Research 2, 1994, pp. 159-225.
15. W Buntine, “Learning classification trees,” Statistics and Computing, 2(2), 1992, pp. 63-73.
1. W Tan, ND Nguyen, L Du, W Buntine, “Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification,” Proceedings of the AAAI Conference on Artificial Intelligence, 2024.
2. ND Nguyen, W Tan, L Du, W Buntine, R Beare, C Chen, “Low-Resource Named Entity Recognition: Can One-vs-All AUC Maximization Help?” Proceedings of the 23th IEEE International Conference on Data Mining, 2023.
3. X Li, M Liu, S Gao, W Buntine, “A survey on out-of-distribution evaluation of neural NLP models,” Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence IJCAI, 2023.
4. ND Nguyen, W Tan, L Du, W Buntine, R Beare, C Chen, “AUC maximization for low-resource named entity recognition,” Proceedings of the AAAI Conference on Artificial Intelligence, 2023.
5. L Zhang, W Buntine, E Shareghi, “On the Effect of Isotropy on VAE Representations of Text”, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022.
6. W Tan, L Du, W Buntine, “Diversity enhanced active learning with strictly proper scoring rules,” Advances in Neural Information Processing Systems 34, 10906-10918, 2021.
7. Kelvin Lo, Yuan Jin, Weicong Tan, Ming Liu, Lan Du, Wray Buntine, “Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence”, The 2021 Conference on Empirical Methods in Natural Language Processing, 2021.
8. He Zhao, Dinh Phung, Viet Huynh, Trung Le & Wray L. Buntine 2021, ‘Neural Topic Model via Optimal Transport’, 9th International Conference on Learning Representations, {ICLR} 2021, Virtual Event, Austria, May 3-7, 2021
9. He Zhao, Dinh Phung, Viet Huynh, Yuan Jin, Lan Du, Wray Buntine, “Topic Modelling Meets Deep Neural Networks: A Survey”, 30th International Joint Conference on Artificial Intelligence, Survey Track, 2021
10. Yuan Jin, He Zhao, Ming Liu, Lan Du, Wray Buntine, “Neural Attention-Aware Hierarchical Topic Model”, The 2021 Conference on Empirical Methods in Natural Language Processing, 2021
11. M 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.
12. WL 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.
13. M Mehrotra, SP Sanner, WL 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.
14. L Du, WL Buntine & M Johnson, Topic Segmentation with a Structured Topic Model,” Proc. of NAACL-HLT 2013, pp. 190-200.
15. D Newman, EV Bonilla & WL Buntine, “Improving topic coherence with regularized topic models,” Advances in Neural Information Processing Systems 24, 2011, p. 1-9
16. W 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.

2006: Docent in Computer Science, University of Helsinki.
1985-1992: University of Technology, Sydney. Ph.D. in Computer Science, “A Theory of Learning Classification Rules”, supervised by Prof. J.R. Quinlan
1983-1984: MQual in Comp.Sc. (1st class) University of Queensland.
1977-1979: B.Sc. in pure and applied honours-level mathematics, University of Queensland.

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.