TrungTin Nguyen

TrungTin Nguyen

Postdoctoral Research Fellow

University of Queensland, Australia

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.

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.

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.
  • 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).

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).

Interests

  • Data Science
  • Statistics
  • Artificial Intelligence

Education

  • Ph.D. in Statistics and Data Science, 2018-2021

    Université de Caen Normandie, France

  • M.S. in Applied Mathematics, 2017-2018

    Université d'Orléans, France

  • B.S. Honors Program in Mathematics and Computer Science, 2013-2017

    Vietnam National University-Ho Chi Minh Univeristy of Science, Vietnam

Publications

(2024). Bayesian Likelihood Free Inference using Mixtures of Experts. In International Joint Conference on Neural Networks, IJCNN 2024.

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(2024). CompeteSMoE--Effective Training of Sparse Mixture of Experts via Competition. arXiv preprint arXiv:2402.02526.

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(2024). Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. arXiv preprint arXiv:2402.02526.

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(2023). Demystifying Softmax Gating Function in Gaussian Mixture of Experts. In Advances in Neural Information Processing Systems, NeurIPS 2023 Spotlight, Acceptance rate 3.6% over 12343 submissions.

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(2023). A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts. arXiv:2310.14188.

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(2023). HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts. In Proceedings of the 2023 Empirical Methods in Natural Language Processing, EMNLP 2023 Main, Acceptance rate 14% over 1041 submissions.

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(2023). Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, AISTATS 2024, Acceptance rate 27.6% over 1980 submissions.

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(2023). Bayesian nonparametric mixture of experts for high-dimensional inverse problems. hal-04015203.

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(2023). Concentration results for approximate Bayesian computation without identifiability. hal-03987197.

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(2023). A non-asymptotic risk bound for model selection in high-dimensional mixture of experts via joint rank and variable selection. In Australasian Joint Conference on Artificial Intelligence 2023, AJCAI 2023 Long Oral Presentation, Acceptance rate 11% over 213 submissions.

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(2022). A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts. Electronic Journal of Statistics.

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(2022). Mixture of expert posterior surrogates for approximate Bayesian computation. 53èmes Journées de Statistique de la Société Française de Statistique (SFdS).

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(2022). Model selection by penalization in mixture of experts models with a non-asymptotic approach. 53èmes Journées de Statistique de la Société Française de Statistique (SFdS).

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(2022). Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces. Communications in Statistics - Theory and Methods.

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(2021). Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models. Journal of Statistical Distributions and Applications.

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(2021). A non-asymptotic model selection in block-diagonal mixture of polynomial experts models. arXiv preprint arXiv:2104.08959.

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(2020). An l1-oracle inequality for the Lasso in mixture-of-experts regression models. arXiv preprint arXiv:2009.10622..

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(2020). Approximation by finite mixtures of continuous density functions that vanish at infinity. Cogent Mathematics & Statistics.

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