Dr Xuan Nguyen
- Data mining, machine learning, bioinformatics (Information theory)
Dr. Vinh Nguyen is a scientist in the domains of data science, data mining and machine learning. He has primarily devoted his research career to inter-disciplinary research, in particular, the development and application of novel data mining and machine learning techniques across diverse problems in medical research, bioinformatics, computational biology, transportation and social networks. His research has resulted in innovative methodologies and techniques that have attracted increasing attention. He has published in total 10 journal papers and 30 conference papers, many of which at top-tier venues in data mining and machine learning (ACM SIGKDD, AAAI, ICML, ICDM, ECML). His work has attracted more than 500 citations in total since 2010. Currently, his research focuses on big data analytics and deep learning.
Dr. Vinh Nguyen obtained a PhD degree in 2011 from UNSW. Prior to joining Melbourne University in June 2013, he spent 3 years at Monash University as a Research Fellow.
- Lei Y, Bezdek J, Chan J, Nguyen X, Romano S, Bailey J. Extending Information-Theoretic Validity Indices for Fuzzy Clustering. IEEE TRANSACTIONS ON FUZZY SYSTEMS. IEE Institute of Electronic Engineers. 2017, Vol. 25, Issue 4.
- Lei Y, Bezdek J, Romano S, Nguyen X, Chan J, Bailey J. Ground truth bias in external cluster validity indices. PATTERN RECOGNITION. Pergamon-Elsevier Science. 2017, Vol. 65.
- Lei Y, Nguyen X, Chan J, Bailey J. rFILTA: relevant and nonredundant view discovery from collections of clusterings via filtering and ranking. KNOWLEDGE AND INFORMATION SYSTEMS. Springer London. 2017, Vol. 52, Issue 1.
- Romano S, Nguyen X, Verspoor C, Bailey J. The randomized information coefficient: assessing dependencies in noisy data. Machine Learning. Kluwer Academic Publishers. 2017.
- Romano S, Nguyen X, Bailey J, Verspoor C. A framework to adjust dependency measure estimates for chance. 16th SIAM International Conference on Data Mining 2016, SDM 2016. 2016.
- Zhou S, Nguyen X, Bailey J, Jia Y, Davidson I. Accelerating online CP decompositions for higher order tensors. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, Vol. 13-17-August-2016.
- Romano S, Nguyen X, Bailey J, Verspoor C. Adjusting for Chance Clustering Comparison Measures. JOURNAL OF MACHINE LEARNING RESEARCH. Journal of Machine Learning Research. 2016, Vol. 17.
- Nguyen X, Zhou S, Chan J, Bailey J. Can high-order dependencies improve mutual information based feature selection?. PATTERN RECOGNITION. Pergamon-Elsevier Science. 2016, Vol. 53.
- Nguyen X, Chan J, Romano S, Bailey J, Leckie C, Kotagiri R, Pei J. Discovering outlying aspects in large datasets. DATA MINING AND KNOWLEDGE DISCOVERY. Kluwer Academic Publishers. 2016, Vol. 30, Issue 6.
- Duan L, Tang G, Pei J, Bailey J, Dong G, Nguyen X, Campbell A, Tang C. Efficient discovery of contrast subspaces for object explanation and characterization. KNOWLEDGE AND INFORMATION SYSTEMS. Springer London. 2016, Vol. 47, Issue 1.
- Romano S, Chelly O, Nguyen X, Bailey J, Houle ME. Measuring Dependency via Intrinsic Dimensionality. 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). IEEE Computer Society. 2016, Issue 4.
- Amsaleg L, Bailey J, Monazam Erfani S, Furon T, Houle ME, RadovanoviĆ M, Nguyen X. The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality. NII Technical Reports. 2016, Issue 5.
- Nguyen X, Monazam Erfani S, Paisitkriangkrai S, Bailey J, Leckie C, Kotagiri R. Training Robust Models Using Random Projection. 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). IEEE Computer Society. 2016.
- Krishnakumar S, Gaudana SB, Nguyen X, Viswanathan GA, Chetty M, Wangikar PP. Coupling of cellular processes and their coordinated oscillations under continuous light in Cyanothece sp. ATCC 51142, a diazotrophic unicellular cyanobacterium. PLoS ONE. Public Library of Science. 2015, Vol. 10, Issue 5.
- Nguyen X, Chan J, Bailey J, Leckie C, Kotagiri R, Pei J. Scalable Outlying-Inlying Aspects Discovery via Feature Ranking. 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Springer Verlag. 2015, Vol. 9078. Editors: Cao T, Lim EP, Zhou ZH, Ho TB, Cheung D, Motoda H.
View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile