Personal profile


Dr Lan Du is a Teaching and Research Academic in the Faculty of IT, Monash Clayton Campus. He is particularly interested in statistical modelling and learning for text analysis, which broadly covers statistical machine leaning, natural language processing, data mining, and social network analysis.

Some specific research topics that he is interested in and currently working on:

  • Probabilistic topic modelling: He is extremely interested in developing various topic models that can explore different discourse structures and other meta information associated with natural lanugage text.
  • Nonparametric Bayesian methods, e.g., Dirichlet process, Pitman-Yor process, and Indian Buffet process. He has been working on developing different sampling methods for Pitman-Yor process since his PhD study at ANU.
  • Inference/optimisation algorithms, e.g., MCMC methods, Variational Bayesian (VB), and various numerical optimisation algorithms
  • Relational learning techniques, e.g., probabilistic matrix factorization, tensor factorization, etc. He is interested in combining matrix factorization techniques with topic models to improve, for example, recommendation systems.
  • Natural Language Processing. He has worked in the NLP group at Macquarie University for about four years. He is interested in how to utlise different lingustic features in topic modelling.

Monash teaching commitment

Dr Lan Du has experience as the Chief Examiner for the following units in the Faculty of IT:

  • FIT5149 Applied data analysis
  • FIT5196 Data wrangling

Lan Du has experience as the Lecturer for the following units in the Faculty of IT:

  • FIT5149 Applied data analysis
  • FIT5196 Data wrangling

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Research Output 2010 2017

Semantic-aware query processing for activity trajectories

Liu, H., Xu, J., Zheng, K., Liu, C., Du, L. & Wu, X. 2 Feb 2017 WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining: February 6–10, 2017, Cambridge, United Kingdom. Tomkins, A. & Zhang, M. (eds.). New York, New York: Association for Computing Machinery (ACM), p. 283-292 10 p.

Research output: ResearchConference Paper

Discriminative sparsity preserving graph embedding

Gou, J., Du, L., Cheng, K. & Cai, Y. 14 Nov 2016 2016 IEEE Congress on Evolutionary Computation (CEC 2016): Vancouver, British Columbia, Canada, 24-29 July 2016, [Proceedings]. Piscataway, NJ : IEEE, Institute of Electrical and Electronics Engineers, p. 4250-4257 8 p. 7744330

Research output: Research - peer-reviewConference Paper

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

A computationally efficient algorithm for learning topical collocation models

Zhao, Z., Du, L., Borschinger, B., Pate, J. K., Ciaramita, M., Steedman, M. & Johnson, M. 2015 53rd Annual Meeting of the Association for Computational Linguistics and 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2015). Zong, C. & Strube, M. (eds.). Stroudsburg Pennsylvania USA: Association for Computational Linguistics, Vol. 1, p. 1460 - 1469 10 p.

Research output: Research - peer-reviewConference Paper

Differential topic models

Chen, C., Buntine, W. L., Ding, N., Xie, L. & Du, L. 2015 In : IEEE Transactions on Pattern Analysis and Machine Intelligence. 37, 2, p. 230 - 242 13 p.

Research output: Research - peer-reviewArticle