Postdoctoral Research Fellow
Overview
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.
I am currently a Postdoctoral Research Fellow at The University of Queensland in the School of Mathematics and Physics from December 2023, where I am 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.
Research Interests
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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. -
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). -
Optimization
Robust and effective optimization algorithms for mixture models (expectation–maximization, variational Bayesian expectation–maximization, 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), biostatistics (genomics, transcriptomics, proteomics), computer vision (image segmentation), quantum chemistry, drug discovery, and materials science (supervised and unsupervised learning on molecular modeling).
Research Impacts
My publications have potential applications in a variety of areas such as: 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), biostatistics (genomics, transcriptomics, proteomics), computer vision (image segmentation), quantum chemistry, drug discovery, and materials science (supervised and unsupervised learning on molecular modeling).
Publications
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Book Chapter: A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection
Nguyen, TrungTin, Nguyen, Dung Ngoc, Nguyen, Hien Duy and Chamroukhi, Faicel (2023). A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection. Lecture Notes in Computer Science. (pp. 234-245) Singapore: Springer Nature Singapore. doi: 10.1007/978-981-99-8391-9_19
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Conference Publication: HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts via HyperNetwork
Do, Giang, Le, Khiem, Pham, Quang, Nguyen, TrungTin, Doan, Thanh-Nam, Nguyen, Binh T., Liu, Chenghao, Ramasamy, Savitha, Li, Xiaoli and Hoi, Steven (2023). HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts via HyperNetwork. Association for Computational Linguistics (ACL).
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Journal Article: Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors
Forbes, Florence, Nguyen, Hien Duy, Nguyen, TrungTin and Arbel, Julyan (2022). Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors. Statistics and Computing, 32 (5) 85, 1-20. doi: 10.1007/s11222-022-10155-6
Publications
Book Chapter
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Nguyen, TrungTin, Nguyen, Dung Ngoc, Nguyen, Hien Duy and Chamroukhi, Faicel (2023). A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection. Lecture Notes in Computer Science. (pp. 234-245) Singapore: Springer Nature Singapore. doi: 10.1007/978-981-99-8391-9_19
Journal Article
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Forbes, Florence, Nguyen, Hien Duy, Nguyen, TrungTin and Arbel, Julyan (2022). Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors. Statistics and Computing, 32 (5) 85, 1-20. doi: 10.1007/s11222-022-10155-6
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Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
Nguyen, TrungTin, Chamroukhi, Faicel, Nguyen, Hien D. and McLachlan, Geoffrey J. (2022). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods, 52 (14), 1-12. doi: 10.1080/03610926.2021.2002360
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Nguyen, Trungtin, Nguyen, Hien Duy, Chamroukhi, Faicel and Forbes, Florence (2022). A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models. Electronic Journal of Statistics, 16 (2), 4742-4822. doi: 10.1214/22-EJS2057
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Nguyen, Hien Duy, Nguyen, TrungTin, Chamroukhi, Faicel and McLachlan, Geoffrey John (2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications, 8 (1) 13. doi: 10.1186/s40488-021-00125-0
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Approximation by finite mixtures of continuous density functions that vanish at infinity
Nguyen, T. Tin, Nguyen, Hien D., Chamroukhi, Faicel and McLachlan, Geoffrey J. (2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics and Statistics, 7 (1). doi: 10.1080/25742558.2020.1750861
Conference Publication
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HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts via HyperNetwork
Do, Giang, Le, Khiem, Pham, Quang, Nguyen, TrungTin, Doan, Thanh-Nam, Nguyen, Binh T., Liu, Chenghao, Ramasamy, Savitha, Li, Xiaoli and Hoi, Steven (2023). HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts via HyperNetwork. Association for Computational Linguistics (ACL).
