1411028
Dr Trung Tin Nguyen
Faculty of Science,
School of Mathematical Sciences
Biography
Hello and welcome! My Vietnamese name is Nguyễn Trung Tín. I therefore used “TrungTin Nguyen” or “Trung Tin Nguyen” in my English publications. The first name is also “Tín” or “Tin” for short. This is Dr. TrungTin Nguyen's institutional website. For the latest updates on my work and publications, please visit my Personal Website.I am currently a MACSYS Postdoctoral Research Fellow (Applied Statistics) at the Queensland University of Technology in the School of Mathematical Sciences and the ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS), starting from February 2025, where I am very fortunate to be mentored by Mathew Simpson, and Christopher Drovandi. I was a Postdoctoral Research Fellow at The University of Queensland in the School of Mathematics and Physics from December 2023 to December 2024, where I was very fortunate to be mentored by Hien Duy Nguyen, and Xin Guo.
Before going to Queensland, I was a Postdoctoral Research Fellow at the Inria centre at the University Grenoble Alpes in the Statify team, where I was very fortunate to be mentored by Florence Forbes, Julyan Arbel, and collaborated with Hien Duy Nguyen as part of an international project team WOMBAT.
I completed my Ph.D. Degree in Statistics and Data Science at Normandie Univ in December 2021, where I was very fortunate to have been advised by Faicel Chamroukhi. During my Ph.D. research, I am grateful to collaborate with Hien Duy Nguyen, and Geoff McLachlan. I received a Visiting PhD Fellowship for 4 months at the Inria centre at the University Grenoble Alpes in the Statify team within a project LANDER.
I have been very fortunate to have had fruitful collaborations since my PhD with Nhat Ho, Huy Nguyen, Khai Nguyen, Quang Pham, Binh Nguyen, Giang Truong Do, Le Huy Khiem, Dung Ngoc Nguyen, and Ho Minh Duy Nguyen (Collabolators in random order).
Personal details
Positions
- MACSYS Post-doctoral Research Fellow (Applied Statistics)
Faculty of Science,
School of Mathematical Sciences
Keywords
Statistics
Qualifications
- PhD in Statistics and Data Science
Professional memberships and associations
Professional Memberships
- 03/2024: Statistical Society of Australia (SSA).
- 08/2021: Institute of Mathematical Statistics (IMS).
- 01/2020: International Society for Bayesian Analysis (ISBA).
- 01/2022: Société Française de Statistique (SFdS).
Editorial Board/Program Committee
- Australian Statistical Conference 2025 (ASC2025): Scientific Program Committee.
- The IEEE World Congress on Computational Intelligence – The International Joint Conference on Neural Networks (IJCNN 2024): Session Chair.
- The Queensland Branch of the Statistical Society of Australia (SSA QLD 2024): General Councillor.
- Research School on Statistics and Data Science (RSSDS 2019, Springer): Program Committee.
- International Journal of Machine Intelligence and Sensory Signal Processing (Inderscience): Associate Editors.
Professional Services
Journal Reviewing
- IEEE Transactions on Information Theory (The Institute of Electrical and Electronics Engineers): 1 paper.
- Electronic Journal of Statistics (Institute of Mathematical Statistics, Bernoulli Society for Mathematical Statistics and Probability): 2 papers.
- Journal of the American Statistical Association (Taylor Francis): 2 papers. Statistics and Computing (Springer): 2 papers.
- Computational Statistics and Data Analysis (Elsevier): 4 papers.
- Neurocomputing (Elsevier): 1 paper.
- Biometrical Journal (Wiley): 2 papers.
- Australian & New Zealand Journal of Statistics (Wiley): 2 paper.
- Communications in Statistics - Theory and Methods (Taylor Francis): 2 papers.
Conference Reviewing
- International Conference on Artificial Intelligence and Statistics (AISTATS): 2 papers.
- International Conference on Learning Representations (ICLR): 3 papers.
- Annual Conference on Neural Information Processing Systems (NeurIPS): 1 paper.
- Annual Meeting of the Association for Computational Linguistics (ACL): 3 papers.
- Proceedings of the Research School on Statistics and Data Science (RSSDS 2019) (Springer): 2 papers.
Teaching
- 07–11/2024: Data Science Capstone Project 1 (DATA7901) (Supervision: 4 Students). Responsible professor: Slava Vaisman, Postgraduate Coursework, The University of Queensland, Australia.
- 02–11/2024: Introduction to Data Science (DATA7001) (Guest Lecturer, Tutorial Content and Practical Session, 30h). Responsible professor: Xin Guo, Postgraduate Coursework, The University of Queensland, Australia.
- 01–04/2023: Statistical analysis and document mining (Lecturer for Complementary Course, 17h). Responsible professor: Pedro Rodrigues, Master 1 of Applied Mathematics, Université Grenoble Alpes, France.
- 09–12/2022: Méthodes statistiques pour la biologie - STA301 (Lecturer and Tutorial Content, 23h). Responsible professor: Julien Chevallier, Licence Sciences et Technologies - BIO, Université Grenoble Alpes, France.
- Fall 2018: Mathematical and numerical foundations of modeling and simulation using partial differential equations (Lecturer for Preparatory Course, 24h). Responsible professor: Jing-Rebecca Li (IDEFIX team, Inria), French-Vietnam Master 2 in Applied Mathematics, VNU-HCM, Vietnam.
- Fall 2017: Principles of Mathematical Analysis (Teaching Assistant, 30h). Responsible professor: Duong Minh Duc, Bachelor in Mathematics and Computer Science, VNU-HCM, Vietnam.
Experience
A central theme of my research is data science, at the intersection of:
- Statistical learning: Model selection (minimal penalties and slope heuristics, non-asymptotic oracle inequalities), simulation-based inference (approximate Bayesian computation, Bayesian synthetic likelihood, method of moments), Bayesian nonparametrics (Gibbs-type priors, Dirichlet process mixture), high-dimensional statistics (variable selection via Lasso and penalization, graphical models), uncertainty estimation, missing data (imputation methods, likelihood-based approaches with missing data).
- Machine learning: Supervised learning (deep hierarchical mixture of experts (DMoE), deep neural networks), unsupervised learning (clustering via mixture models, dimensionality reduction via principal component analysis, deep generative models via variational autoencoders, generative adversarial networks and normalizing flows), reinforcement learning (partially observable Markov decision process), structured prediction (probabilistic graphical models).
- Optimization: Robust and effective optimization algorithms for mixture models (MM algorithm, expectation–maximization, variational Bayesian inference, Markov chain Monte Carlo methods), difference of convex algorithm, optimal transport (Wasserstein distance, voronoi loss function).
- Applications: Natural language processing (large language model), remote sensing (planetary science, e.g., retrieval of Mars surface physical properties from hyper-spectral images), signal processing (sound source localization), biological data (genomics, transcriptomics, proteomics, cellular systems), computer vision (image segmentation), quantum chemistry, drug discovery, and materials science (supervised and unsupervised learning on molecular modeling).
Publications
Research outputs by year
QUT ePrints
For more publications by Trung Tin, explore their research in QUT ePrints (our digital repository).
Supervision
Looking for a postgraduate research supervisor?
I am currently accepting research students for Honours, Masters and PhD study.
You can browse existing student topics offered by QUT or propose your own topic.