Projects per year
Personal profile
Biography
Dr Lan Du is a Teaching and Research Academic in the Faculty of IT, Monash Clayton Campus. He is particularly interested in statistical modeling and learning for text analysis, which broadly covers statistical machine learning, natural language processing, data mining, and social network analysis. My up-to-date information can be found here
Some specific research topics that he is interested in and currently working on:
- Probabilistic topic modeling: He is extremely interested in developing various topic models that can explore different discourse structures and other meta information associated with natural language 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 Ph.D. study.
- Inference/ optimization algorithms, e.g., MCMC methods, Variational Bayesian (VB), and various numerical optimization 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 utilize different linguistic features in topic modeling.
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
Research area keywords
- machine learning
- text analysis
- topic modeling
- discrete data analysis
- Non Parametric Methods
- Ranking method
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Projects 2019 2020
- 1 Active
Research Output 2008 2019
Leveraging external information in topic modelling
Zhao, H., Du, L., Buntine, W. & Liu, G., 2019, (Accepted/In press) In : Knowledge and Information Systems. p. 1-33 33 p.Research output: Contribution to journal › Article › Research › peer-review
Leveraging meta information in short text aggregation
Zhao, H., Du, L., Liu, G. & Buntine, W., Jul 2019, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Korhonen, A., Traum, D. & Màrquez, L. (eds.). Florence Italy: Association for Computational Linguistics (ACL), p. 4042-4049 8 p. P19-1396Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access
File
Representation-based classification methods with enhanced linear reconstruction measures for face recognition
Gou, J., Song, J., Ou, W., Zeng, S., Yuan, Y. & Du, L., Oct 2019, In : Computers and Electrical Engineering. 79, 15 p., 106451.Research output: Contribution to journal › Article › Research › peer-review
Bayesian Multi-label Learning with Sparse Features and Labels, and Label Co-occurrences
Zhao, H., Rai, P., Du, L. & Buntine, W., 2018, 2018 Twenty-First International Conference on Artificial Intelligence and Statistics, AISTATS 2018 : 9-11 April 2018, Lanzarote, Canary Islands, Proceedings . Storkey, A. & Perez-Cruz, F. (eds.). USA: Proceedings of Machine Learning Research (PMLR), p. 1943-1951 9 p. (Proceedings of Machine Learning Research; vol. 84).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access
Dirichlet belief networks for topic structure learning
Zhao, H., Du, L., Buntine, W. & Zhou, M., 2018, NIPS Proceedings: Advances in Neural Information Processing Systems 31 (NIPS 2018). Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N. & Garnett, R. (eds.). San Diego CA USA: Neural Information Processing Systems (NIPS), p. 7966-7977 12 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter (Book) › Research › peer-review
Open Access
File