Portrait of Hien Duy Nguyen

Hien Duy Nguyen

Associate Professor, La Trobe University · Professor (IMI), Kyushu University

Email: h.nguyen5@latrobe.edu.au  ·  CV: Download

ORCID: 0000-0002-9958-432X  ·  ResearchGate  ·  GitHub

Contact Details

Primary affiliation

Dr. Hien Duy Nguyen
Associate Professor
School of Computing, Engineering and Mathematical Sciences
La Trobe University
Bundoora, Victoria, Australia

Profiles & links

Brief Bio

Hien Nguyen holds dual positions as an Associate Professor at La Trobe University in Australia and a Professor at the Institute of Mathematics for Industry at Kyushu University in Japan. His role focuses on building collaborative relationships between the two institutions and the two countries at large to foster new innovative research in mathematical and computational statistics, as well as the applications of such developments. Hien also holds an Honorary Associate Professorship at his Alma Mater, the University of Queensland in Brisbane, Australia, where he continues to supervise students.

Hien has a strong interest in the research of computational statistics and its interaction with the fields of machine learning and AI. His research focuses on the analysis of large datasets that are acquired incrementally or are too large for conventional computers. In such settings, his work centres on the use of methods such as stochastic optimisation theory and Bayesian inference to handle such difficulties. He is also interested in the development of rigorous statistical tools for choosing between models when data are complex and for testing the validity of such choices, as well as providing confidence statements regarding the goodness of inferential decisions in practice.

Hien’s methods have found applications in multiple areas of applied research, including neuroimaging, agriculture, genetics and genomics, proteomics, quantum computing, economics, public policy, and social sciences. Outside of his work, Hien Nguyen currently holds editorial roles with the Australian and New Zealand Journal of Statistics, Heliyon Mathematics, the International Journal of Mathematics for Industry, and Statistical Analysis and Data Mining.

In his free time, Hien Nguyen enjoys bushwalking and spending time with his three cats: Nyx, Penelope, and Beau.

Roles

  • Professor at the Institute of Mathematics for Industry, Kyushu University in Fukuoka, Japan (2023–Present)
  • Associate Professor at La Trobe University in Melbourne, Australia (2023–Present)
  • Honorary Associate Professor at the University of Queensland in Brisbane, Australia (2023–Present)
  • Executive Editor of Statistical Analysis and Data Mining (2024–Present)
  • Associate Editor of Heliyon Mathematics (2026-Present)
  • Associate Editor of the International Journal of Mathematics for Industry (2024–Present)
  • Book Review Editor of the Australian and New Zealand Journal of Statistics (2023–Present)
  • Technical Editor of the Australian and New Zealand Journal of Statistics (2018–Present)

Major Projects

Australia’s Economic Accelerator (AEA) Innovate Program IV240100099 (with Aniruddha Desai, Krishnakumar Santhanam, Lalit Yagnik, Mark Jois, Erik van Vulpen, Aidin Bervan, Shanmuga Sundar Dhanabalan, and Phat Huynh): Fully integrated digital technology solution for enhanced dairy farm management.

2025–2027

  • This project develops a fully integrated digital technology solution for enhanced dairy farm management.

ARC DP250100860 (with Shu-Kay Angus Ng, Griffith University; Florence Forbes, Inria; Liming Xiang, Nanyang Technological University): Advancing statistical models for clustering data with structured dependence.

2025–2027

  • This project develops statistical models and algorithms for clustering data with structured dependence.

STARFISH Activity (with Rebecca Chisholm, Mark Davies, Tim Barnett, David Price, Trish Campbell, and Joel Miller): Optimising targeted investigations of Strep A transmission and Environmental Health Initiatives (EHIs) assessment: a model-based approach.

2024–2026

  • This project develops model-based methods for targeted investigations of Strep A transmission and Environmental Health Initiatives assessment.

Inria Associate Teams WOMBAT (with Florence Forbes, Inria): Variance-reduced Optimization Methods and Bayesian Approximation Techniques for scalable inference.

2023–2026

  • This project develops variance-reduced optimisation methods and Bayesian approximation techniques for scalable inference.

ARC DP230100905 (with Xin Guo, University of Queensland; Florence Forbes, Inria; Gersende Fort, CNRS): Stochastic majorization–minimization algorithms for data science.

2023–2025

  • This project develops stochastic majorization–minimization algorithms for large-scale data science problems.

Past Projects

Australian Government Road Safety Innovation Fund (with Long Truong, La Trobe University): Development of planning-stage crash prediction tools for use at the planning stages.

2021–2023

  • This project developed planning-stage crash prediction tools for use in transport planning.

Inria Associate Teams LANDER (with Florence Forbes, Inria): Latent analysis, adversarial networks, and DimEnsionality Reduction.

2019–2021

  • This project supported collaborative work on latent analysis, adversarial networks, and dimensionality reduction.

ARC DP180101192 (with Geoffrey McLachlan, University of Queensland; and Sharon Lee, University of Queensland): Classification methods for providing personalised and class decisions.

2018–2021

  • This project developed classification methods for providing personalised and class decisions.

ARC DE170101134: Feasible algorithms for big inference.

2017–2020

  • This project developed feasible algorithms for big inference.

Publications

Fort, G., Forbes, F., & Nguyen, H. D. (2026). Sequential Sample Average Majorization–Minimization. Statistics and Computing, 36(1), 31. https://doi.org/10.1007/s11222-025-10780-x
Gao, H., Wang, D., & Nguyen, H. D. (2026). An efficient online monitoring method for high-dimensional data based on interpoint distance algorithm: an online monitoring method for high-dimensional data. Quality and Reliability Engineering International, 42(3), 1088–1115. https://doi.org/10.1002/qre.70123
Chan, K., Weir, T., Freer, E., Ong, D., & Nguyen, H. (2025). Predictability of incisal labiolingual inclination, overjet, and overbite changes, and the prevalence of open gingival embrasures in patients with mandibular incisor extraction treated with Invisalign: A retrospective cohort study. American Journal of Orthodontics and Dentofacial Orthopedics, 168(2), 199–209. https://doi.org/10.1016/j.ajodo.2025.02.015
Clarke, N., Urchs, S., Nguyen, H. D., Moreau, C., Dansereau, C., Tam, A., Evans, A. C., & Bellec, L. (2025). High-precision machine learning identifies a reproducible functional connectivity signature of autism spectrum diagnosis in a subset of individuals. GigaScience, 14, giaf091.
Goh, P. K. T., Nguyen, H., & Olive, R. (2025). Treatment duration for impacted maxillary canines: a comparison between surgical exposure and conservative non-surgical orthodontic space opening. Australasian Orthodontic Journal, 41(1), 392–405. https://doi.org/10.2478/aoj-2025-0038
Ho, K. L. K., & Nguyen, H. D. (2025). Variable selection via thresholding. Scandinavian Journal of Statistics, 53(1), 207–237. https://doi.org/10.1111/sjos.70032
Hoe, A., Freer, E., Goh, P. K. T., Ong, D., Nguyen, H., & Weir, T. (2025). Predictability of mesiodistal tip of maxillary central incisors in clear aligner therapy. American Journal of Orthodontics and Dentofacial Orthopedics, 167(5), 568–577. https://doi.org/10.1016/j.ajodo.2024.12.005
Nguyen, H., Nguyen, T. T., Arbel, J., & Forbes, F. (2025). Revisiting concentration results for approximate Bayesian computation. Bayesian Analysis, 1–23. https://doi.org/10.1214/25-BA1520
Roohi, S., Skarbez, R., & Nguyen, H. (2025). Conformal affective text prediction: Estimating uncertainty in emotion and valence intensity. Proceedings of the 13th International Conference on Affective Computing and Intelligent Interaction (ACII 2025).
To, X. V., Nguyen, H. D., Cumming, P., Curpen, P., & Nasrallah, F. A. (2025). Contemporaneous Outcomes Multiple Imputation (KOMI) can communicate missing Glasgow Outcome Scale – Extended (GOSE) scores: A multi-domain imputation for GOSE in the transforming research and clinical knowledge in traumatic brain injury (TRACK-TBI) data. Journal of the Neurological Sciences, 475(123564), 1–12. https://doi.org/10.1016/j.jns.2025.123564
Almasi, F., Stear, M. J., Khansefid, M., Nguyen, H., Desai, A., & Pryce, J. E. (2024). Innovative use of sensor technology to study grazing behaviour and its associations with parasitic resistance in sheep. Small Ruminant Research, 232(107223).
Chiu Chong, M., Nguyen, H. D., & Nguyen, T. (2024). Risk bounds for mixture density estimation on compact Domains via the h-lifted Kullback–Leibler divergence. Transactions of Machine Learning Research, (OpenReview: lAKvQO4vHj).
Duong, Q., Gilbert, H., & Nguyen, H. (2024). A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne. Accident Analysis and Prevention, 207(107747), 1–19. https://doi.org/10.1016/j.aap.2024.107747
Forbes, F., Nguyen, H. D., & Nguyen, T. T. (2024). Bayesian likelihood free inference using mixtures of experts. Proceedings of the International Joint Conference on Neural Networks.
Fryer, D., Lowing, D., Strümke, I., & Nguyen, H. (2024). Multi-choice Solutions for Feature and Parameter Importance. Proceedings of the 22nd Australasian Data Science and Machine Learning Conference, 48–62.
Goh, P. K. T., Pulemotov, A., Nguyen, H., Pinto, N., & Olive, R. (2024). Treatment duration by morphology and location of impacted maxillary canines: a CBCT investigation. American Journal of Orthodontics and Dentofacial Orthopedics, 166, 160–170.
Grot, S., Smine, S., Potvin, S., Darcey, M., Pavlov, V., Genon, S., Nguyen, H., & Orban, P. (2024). Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 131(110950).
Nguyen, H. D. (2024). Contribution to the discussion of paper by I Waudby-Smith and A. Ramdas. Journal of the Royal Statistical Society B, 86.
Nguyen, T. T., Forbes, F., Arbel, J., & Nguyen, H. D. (2024). Bayesian nonparametric mixture of experts for inverse problems. Journal of Nonparametric Statistics, 1–60. https://doi.org/10.1080/10485252.2024.2426091
Roohi, S., Skarbez, R., & Nguyen, H. D. (2024). Beyond Factualism: a study of LLM calibration through the lens of conversational emotion recognition. Proceedings of the 37th Australasian Joint Conference on Artificial Intelligence.
Truong, L., Weir, T., Nguyen, H., Freer, E., & Ong, D. (2024). Mesiodistal tip expression of mandibular anterior teeth in patients with mandibular incisor extraction treated with Invisalign aligners. American Journal of Orthodontics and Dentofacial Orthopedics, 166(6), 538–548. https://doi.org/10.1016/j.ajodo.2024.07.016
Westerhout, J., Nguyen, T. T., Guo, X., & Nguyen, H. D. (2024). On the asymptotic distribution of the minimum empirical risk. Forty-first International Conference on Machine Learning.
Almasi, F., Stear, M., Khansefid, M., Nguyen, H., Desai, A., & Pryce, J. E. (2023). The repeatability and heritability of traits derived from accelerometer sensors associated with grazing and rumination time in an extensive sheep farming system. Frontiers in Animal Science, 4(1154797).
Arbel, J., Girard, S., Nguyen, H. D., & Usseglio-Carleve, A. (2023). Multivariate expectile-based distribution: properties, Bayesian inference, and applications. Journal of Statistical Planning and Inference, 225, 146–170.
Fryer, D., Lowing, D., Strümke, I., & Nguyen, H. (2023). Multi-choice Explanations: a new cooperative game structure for XAI. IJCNN Workshop on Trustworthy and Responsible AI: Theory, Applications and Challanges.
Navarathna, R., Le, D. T., Hamann, A. R., Nguyen, H. D., Stace, T. M., & Fedorov, A. (2023). Passive superconducting circulator on a chip. Physical Review Letters, 130, 037001.
Nguyen, H. D., Fryer, D., & McLachlan, G. J. (2023). Order selection with confidence for finite mixture models. Journal of the Korean Statistical Society, 52, 154–184.
Nguyen, H. D., & Gupta, M. (2023). Finite sample inference for empirical Bayesian methods. Scandinavian Journal of Statistics, 50, 1616–1640.
Nguyen, T., Nguyen, D. N., Nguyen, H. D., & Chamroukhi, F. (2023). A non-asymptotic risk bound for model selection in high-dimensional mixture of experts via joint rank and variable selection. In Proceedings of the Australasian Jount Conference on Artificial Intelligence (AJCAI). Springer.
Roohi, S., Skarbez, R., & Nguyen, H. (2023). Reliable emotion recognition in conversation: Quantifying and communicating uncertainty. IJCNN Workshop on Trustworthy and Responsible AI: Theory, Applications and Challanges.
Wallis, T. P., Jiang, A., Young, K., Hou, H., Kudo, K., McCann, A. J., Durisic, N., Joensuu, M., Oelz, D., Nguyen, H., Gormal, R. S., & Meunier, F. A. (2023). Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing. Nature Communications, 14, 3353.
Almasi, F., Khansefid, M., Nguyen, H., Desai, A., Pryce, J. E., & Stear, M. (2022). Repeatability estimates of grazing and rumination activity of Merino sheep measured using wearable sensors. Proceedings of the World Congress on Genetics Applied to Livestock Production.
Almasi, F., Nguyen, H., Heydarian, D., Sohi, R., Nikbin, S., Jenvey, C. J., Halliwell, E., Ponnampalam, E. N., Desai, A., Jois, M., & Stear, M. J. (2022). Quantification of behavioural variation among sheep grazing on pasture using accelerometer sensors. Animal Production Science, 62, 1527–1538.
Durand, J.-B., Forbes, F., Phan, C. D., Truong, L., Nguyen, H. D., & Dama, F. (2022). Bayesian nonparametric spatial prior for traffic crash risk mapping: a case study of Victoria, Australia. Australian and New Zealand Journal of Statistics, 64, 171–204.
Forbes, F., Nguyen, H. D., Nguyen, T. T., & Arbel, J. (2022). Approximate Bayesian computation with surrogate posteriors. Statistics and Computing, 32, 85.
Forbes, F., Nguyen, H. D., Nguyen, T. T., & Arbel, J. (2022). Mixture of expert posterior surrogates for approximate Bayesian computation. 53emes Journees de Statistique de La Societe Francaise de Statistique (SFdS).
Gao, J., Burgard, D. A., Tscharke, B. J., Lai, F. Y., O’Brien, J. W., Nguyen, H. D., Zheng, Q., Li, J., Du, P., Li, X., Wang, D., Castiglioni, S., Cruz-Cruz, C., Baz-Lomba, J. A., Yargeau, V., Emke, E., Thomas, K. V., Mueller, J. F., & Thai, P. K. (2022). Refining the estimation of amphetamine consumption by wastewater-based epidemiology. Water Research, 225, 119182.
Nguyen, H., Forbes, F., Fort, G., & Cappe, O. (2022). An online Minorization–Maximization Algorithm. Proceedings of the International Federation of Classification Societies.
Nguyen, H., Lee, S., & Forbes, F. (2022). A Festschrift for Geoff McLachlan. Australian and New Zealand Journal of Statistics, 64, 111–116.
Nguyen, T. T., Chamroukhi, F., Nguyen, H. D., & Forbes, F. (2022). Model selection by penalization in mixture of experts models with a non-asymptotic approach. 53emes Journees de Statistique de La Societe Française de Statistique (SFdS).
Nguyen, T. T., Nguyen, H. D., Chamroukhi, F., & Forbes, F. (2022). A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts. Electronic Journal of Statistics, 16, 4742–4822.
Phan, D. C., Truong, L. T., Nguyen, H. D., & Tay, R. (2022). Modelling the safety effects of train commuters’ access modes. Journal of Advanced Transportation, 2022, 3473397.
Sohi, R., Carroll, A., Nguyen, H., Almasi, Z., Miller, J., Trompf, J., Bervan, A., Godoy, B. I., Stear, M., Desai, A., & Jois, M. (2022). Determination of ewe behaviour around lambing time and prediction of parturition seven days prior to lambing by tri-axial accelerometer sensors in an extensive farming system. Animal Production Science, 62, 1729–1738.
Truong, L., & Nguyen, H. D. (2022). Traffic congestion and road safety in urban areas: an analysis of Melbourne. Australasian Transport Research Forum (ATRF), 43rd, 2022, Adelaide, South Australia, Australia.
Urchs, S., Tam, A., Orban, P., Moreau, C., Benhajali, Y., Nguyen, H. D., Evans, A. C., & Bellec, P. (2022). Subtypes of functional connectivity associate robustly with ASD diagnosis. eLife, 11, e56257.
Fryer, D. V., Strümke, I., & Nguyen, H. (2021). Model independent feature attributions: Shapley values that uncover non-linear dependencies. PeerJ Computer Science, 7(e582).
Fryer, D., Strümke, I., & Nguyen, H. (2021). Shapley values for feature selection: the good, the bad, and the axioms. IEEE Access, 9, 144352–144360.
Gray, R., Nguyen, H., Bressington, D., Jones, M., & Thompson, D. (2021). Comment on - Mothers’ voices and white noise on premature infants’ psychological reactions in a neonatal intensive care unit: A multi-arm randomised controlled trial. International Journal of Nursing Studies, (104050).
McLachlan, G. J., Ng, S. K., & Nguyen, H. D. (2021). EM Algorithm. In Wiley StatsRef: Statistics reference online. Wiley.
Nguyen, H. D. (2021). Finite sample inference for generic autoregressive models. Proceedings of FMfI 2021, Math-for-Industry Lecture Notes.
Nguyen, H. D., Bagnall-Guerreiro, J., & Jones, A. T. (2021). Universal inference with composite likelihoods. Proceedings of the 63rd ISI World Statistics Congress.
Nguyen, H. D., Nguyen, T. T., Chamroukhi, F., & McLachlan, G. (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications, 8(13).
Phan, D. C., Truong, L. T., Nguyen, H. D., & Tay, R. (2021). Can walking and cycling for train access improve road safety? Australian Road Safety Conference (ARSC2021).
Arbel, J., Marchal, O., & Nguyen, H. D. (2020). On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables. ESAIM: Probability and Statistics, 24, 39–55.
Bagnall, J., Jones, A., Karavarsamis, N., & Nguyen, H. (2020). The fully-visible Boltzmann machine and the Senate of the 45th Australian Parliament in 2016. Journal of Computational Social Science, 3, 55–81.
Fryer, D., Nguyen, H., & Castellazzi, P. (2020). \(k\)-means on positive definite matrices, and an application to clustering in radar image sequences. Proceedings of the IEEE Symposium Series on Computational Intelligence.
Nguyen, H. D., Arbel, J., Lü, H., & Forbes, F. (2020). Approximate Bayesian computation via the energy statistic. IEEE Access, 8, 131683–131698.
Nguyen, H. D., Forbes, F., & McLachlan, G. J. (2020). Mini-batch learning of exponential family finite mixture models. Statistics and Computing, 30, 731–748.
Nguyen, T. T., Nguyen, H. D., Chamroukhi, F., & McLachlan, G. J. (2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics and Statistics, 7, 1750861.
Redivo, E., Nguyen, H., & Gupta, M. (2020). Bayesian clustering of skewed and multimodal data using geometric skew normal distributions. Computational Statistics and Data Analysis, 152, 107044.
Vladimirova, M., Girard, S., Nguyen, H., & Arbel, J. (2020). Sub-Weibull distributions: generalizing sub-Gaussian and sub-Exponential properties to heavier-tailed distributions. Stat, 9, e318.
Chamroukhi, F., Lecocq, F., & Nguyen, H. D. (2019). Regularized estimation and feature selection in mixtures of Gaussian-gated experts models. Proceedings of the Research School on Statistics and Data Science (RSSDS).
Chamroukhi, F., & Nguyen, H. D. (2019). Model-based clustering and classification of functional data. WIREs Data Mining and Knowledge Discovery, e1298.
Fryer, D., Nguyen, H., & Orban, P. (2019). studentlife: tidy handling and navigation of a valuable mobile-health dataset. Journal of Open Source Software, 4(40). 10.21105/joss.01587
Jones, A. T., Bagnall, J. J., & Nguyen, H. D. (2019). BoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machines. Journal of Open Source Software, 4, 1193.
Jones, A. T., Nguyen, H. D., & Bagnall, J. J. (2019). BoltzMM: Boltzmann Machines with MM Algorithms. Software published in the Comprehensive R Archive Network.
Nguyen, H. (Ed.). (2019). Statistics and Data Science: Proceedings of the 2019 Research School on Statistics and Data Science (RSSDS). Springer.
Nguyen, H. D. (2019). An introduction to approximate Bayesian computation. Proceedings of the Research School on Statistics and Data Science (RSSDS).
Nguyen, H. D. (2019). Asymptotic normality of the time-domain generalized least squares estimator for linear regression models. Stat, 8(e248).
Nguyen, H. D., Chamroukhi, F., & Forbes, F. (2019). Approximation results regarding the multiple-output mixture of linear experts model. Neurocomputing, 366, 208–214.
Nguyen, H. D., & McLachlan, G. J. (2019). On approximation via convolution-defined mixture models. Communications in Statistics - Theory and Methods, 48, 3945–3955.
Nguyen, H. D., Yee, Y., McLachlan, G. J., & Lerch, J. P. (2019). False discovery rate control for grouped or discretely supported p-values with application to a neuroimaging study. SORT, 43, 1–22.
Truong, L., Nguyen, H., Nguyen, H., & Vu, H. (2019). Pedestrian overpass use and its relationship with digital and social distractions, and overpass characteristics. Accident Analysis and Prevention, 131, 234–238.
Bagnall, J., Jones, A. T., & Nguyen, H. (2018). Analysing the voting patterns of the Senate of the 45th Australian Parliament via fully-visible Boltzmann machines. Poster presented at UseR! 2018.
Jones, A. T., Nguyen, H. D., & McLachlan, G. J. (2018). logKDE: log-transformed kernel density estimation. Journal of Open Source Software, 3, 870.
Lloyd-Jones, L. R., Nguyen, H. D., & McLachlan, G. J. (2018). A globally convergent algorithm for lasso-penalized mixture of linear regression models. Computational Statistics and Data Analysis, 119, 19–38.
Nguyen, H. D. (2018). Near universal consistency of the maximum pseudolikelihood estimator for discrete models. Journal of the Korean Statistical Society, 47, 90–98.
Nguyen, H. D., & Chamroukhi, F. (2018). An introduction to the practical and theoretical aspects of mixture-of-experts modeling. WIREs Data Mining and Knowledge Discovery, e1246.
Nguyen, H. D., & Jones, A. T. (2018). Big data-appropriate clustering via stochastic approximation and Gaussian mixture models. In Data Analytics: Concepts, Techniques, and Applications. CRC Press.
Nguyen, H. D., Jones, A. T., & McLachlan, G. J. (2018). logKDE: Computing log-transformed kernel density estmates for positive data. Software published in the Comprehensive R Archive Network.
Nguyen, H. D., Jones, A. T., & McLachlan, G. J. (2018). Positive data kernel density estimation via the logKDE package for R. Proceedings of the Sixteenth Australasian Data Mining Conference.
Nguyen, H. D., Jones, A. T., & McLachlan, G. J. (2018). Stream-suitable optimization algorithms for some soft-margin support vector machine variants. Japanese Journal of Statistics and Data Science, 1, 81–108.
Nguyen, H. D., & McLachlan, G. J. (2018). Chunked-and-averaged estimators for vector parameters. Statistics and Probability Letters, 137, 336–342.
Nguyen, H. D., & McLachlan, G. J. (2018). Some theoretical results regarding the polygonal distribution. Communications in Statistics - Theory and Methods, 47, 5083–5095.
Nguyen, H. D., McLachlan, G. J., Ullmann, J. F. P., Voleti, V., Li, W., Hillman, E. M. C., Reutens, D. C., & Janke, A. L. (2018). Whole-volume clustering of time series data from zebrafish brain calcium images via mxiture modeling. Statistical Analysis and Data Mining, 11, 5–16.
Nguyen, H. D., Wang, D. H., & McLachlan, G. J. (2018). Randomized mixture models for probability density approximation and estimation. Information Sciences, 467, 135–148.
Orban, P., Dansereau, C., Desbois, L., Mongeau-Perusse, V., Giguere, C.-E., Nguyen, H., Mendrek, A., Stip, E., & Bellec, P. (2018). Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity. Schizophrenia Research, 192, 167–171.
McLachlan, G. J., & Nguyen, H. D. (2017). Contribution to the discussion of paper by M. Drton and M. Plummer. Journal of the Royal Statistical Society B, 79, 365.
Nguyen, H. D. (2017). A novel algorithm for clustering of data on the unit sphere via mixture models. JSM Proceedings: Statistical Computing Section.
Nguyen, H. D. (2017). An introduction to MM algorithms for machine learning and statistical estimation. WIREs Data Mining and Knowledge Discovery, 7(e1198).
Nguyen, H. D., & McLachlan, G. J. (2017). Iteratively-reweighted least-squares fitting of support vector machines: a majorization-minimization algorithm approach. Proceedings of the 2017 Future Technologies Conference (FTC).
Nguyen, H. D., & McLachlan, G. J. (2017). Progress on a conjecture regarding the triangular distribution. Communications in Statistics - Theory and Methods, 46, 11261–11271.
Nguyen, H. D., McLachlan, G. J., & Hill, M. M. (2017). Statistical evaluation of labeled comparative profiling proteomics experiments using permutation test. In Proteome bioinformatics. Springer.
Nguyen, H. D., McLachlan, G. J., Orban, P., Bellec, P., & Janke, A. L. (2017). Maximum pseudolikelihood estimation for a model-based clustering of time series data. Neural Computation, 29, 990–1020.
Oyarzun, C., Sanjurjo, A., & Nguyen, H. (2017). Response functions. European Economic Review, 98, 1–31.
Jones, A. T., & Nguyen, H. D. (2016). lowmemtkmeans: Low memory use trimmed k-means. Software published in the Comprehensive R Archive Network.
Lloyd-Jones, L. R., Nguyen, H. D., McLachlan, G. J., Sumpton, W., & Wang, Y.-G. (2016). Mixture of time dependent growth models with an application to blue swimmer crab length-frequency data. Biometrics, 72, 1255–1265.
Nguyen, H. D., Lloyd-Jones, L. R., & McLachlan, G. J. (2016). A block minorization-maximization algorithm for heteroscedastic regression. IEEE Signal Processing Letters, 23, 1031–1135.
Nguyen, H. D., Lloyd-Jones, L. R., & McLachlan, G. J. (2016). A universal approximation theorem for mixture-of-experts models. Neural Computation, 28, 2585–2593.
Nguyen, H. D., & McLachlan, G. J. (2016). Laplace mixture of linear experts. Computational Statistics and Data Analysis, 93, 177–191.
Nguyen, H. D., & McLachlan, G. J. (2016). Linear mixed models with marginally symmetric nonparametric random-effects. Computational Statistics and Data Analysis, 106, 151–169.
Nguyen, H. D., & McLachlan, G. J. (2016). Maximum likelihood estimation of triangular and polygonal distributions. Computational Statistics and Data Analysis, 106, 23–36.
Nguyen, H. D., McLachlan, G. J., Ullmann, J. F. P., & Janke, A. L. (2016). Laplace mixture autoregressive models. Statistics and Probability Letters, 110, 18–24.
Nguyen, H. D., McLachlan, G. J., Ullmann, J. F. P., & Janke, A. L. (2016). Spatial clustering of time-series via mixture of autoregressions models and Markov Random Fields. Statistica Neerlandica, 70, 414–439.
Nguyen, H. D., McLachlan, G. J., & Wood, I. A. (2016). Mixtures of spatial spline regressions for clustering and classification. Computational Statistics and Data Analysis, 93, 76–85.
Nguyen, H. D., & Wood, I. A. (2016). A block successive lower-bound maximization algorithm for the maximum pseudolikelihood estimation of fully visible Boltzmann machines. Neural Computation, 28, 485–492.
Nguyen, H. D., & Wood, I. A. (2016). Asymptotic normality of the maximum pseudolikelihood estimator for fully visible Boltzmann machines. IEEE Transactions on Neural Networks and Learning Systems, 27, 897–902.
Nguyen, H. D. (2015). Finite mixture models for regression problems [PhD thesis]. University of Queensland.
Nguyen, H. D. (2015). NostalgiR: Advanced text-based plots. Software published in the Comprehensive R Archive Network.
Nguyen, H. D., & McLachlan, G. J. (2015). Maximum likelihood estimation of Gaussian mixture models without matrix operations. Advances in Data Analysis and Classification, 9, 371–394.
Chen, D., Shah, A., Nguyen, H., Loo, D., Inder, K., & Hill, M. (2014). Online quantitative proteomics p-value calculator for permutation-based statistical testing of peptide ratios. Journal of Proteomics Research, 13, 4184–4191.
Lloyd-Jones, L. R., Nguyen, H. D., Wang, Y.-G., & O’Neill, M. F. (2014). Improved estimation of size-transition matrices using tag-recapture data. Canadian Journal of Fisheries and Aquatic Sciences, 71, 1385–1394.
Nguyen, H. D., & McLachlan, G. J. (2014). Asymptotic inference for hidden process regression models. Proceedings of the IEEE Statistical Signal Processing Workshop.
Nguyen, H. D., McLachlan, G. J., Cherbuin, N., & Janke, A. L. (2014). False discovery rate control in magnetic resonance imaging studies via Markov random fields. IEEE Transactions on Medical Imaging, 33, 1735–1748.
Nguyen, H. D., Janke, A. L., Cherbuin, N., McLachlan, G. J., Sachdev, P., & Anstey, K. J. (2013). Spatial false discovery rate control for magnetic resonance imaging studies. Proceedings of the 2013 Digital Imaging: Techniques and Applications (DICTA) Conference.
Inder, K. L., Zheng, Y. Z., Davis, M. J., Moon, H., Loo, D., Nguyen, H., Clements, J. A., Parton, R. G., Foster, L. J., & Hill, M. M. (2012). Expression of PRTF in PC-3 cells modulated cholesterol dynamics and actin cytoskeleton impacting secretion pathways. Molecular and Cellular Proteomics, 11(M111.012245).
Nguyen, H. D., Hill, M. M., & Wood, I. A. (2012). A robust permutation test for quantitative SILAC proteomics experiments. Journal of Integrated OMICS, 2(80-93).
Nguyen, H. D., & Wood, I. A. (2012). Variable selection in statistical models using population-based incremental learning with applications to genome-wide association studies. Proceedings of the 2012 IEEE Congress on Evolutionary Computation (CEC).