TrungTin Nguyen Mathematical Statistics · AI for Science
Mathematical Statistics · AI for Science · Mixture of Experts

TrungTin Nguyen

Vietnamese name: Nguyễn Trung Tín. I am a mathematical statistician working at the interface of statistical theory, machine learning, and AI for science. My research develops principled methods for model selection, uncertainty quantification, and mixture-of-experts learning, with applications in genomics, systems biology, and trustworthy AI.

Selected publicationsAcademic ProfileShort CV
Mixture-of-experts theoryBayesian uncertainty quantificationTrustworthy statistical machine learningAI for scientific discovery
Portrait of TrungTin Nguyen

Profile highlights

Current rolePostdoctoral Research Fellow / Lecturer, QUT
Research baseSchool of Mathematical Sciences, ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS), and QUT Centre for Data Science
Research themesMathematical statistics, AI for science, uncertainty-aware machine learning
Scholarly record40 outputs total; h-index 13; i10-index 14
Publication profile10 journal papers, 17 conference papers, 4 workshop papers, 1 PhD thesis, and 8 preprints
Recent highlightsAISTATS 2026 Spotlight, NeurIPS 2025, ICLR 2026, Bayesian Analysis, Econometrics and Statistics

About

I build rigorous statistical and machine learning methods that combine mathematical foundations with real scientific impact. My recent work focuses on model selection, mixture-of-experts models, uncertainty quantification, and scalable learning algorithms for heterogeneous and high-dimensional data.

Biography

I am currently a Postdoctoral Research Fellow / Lecturer (Level B) in the School of Mathematical Sciences at Queensland University of Technology, Brisbane, Australia. I contribute to the ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS) and the QUT Centre for Data Science, where I work on foundational methods in data science and AI.

My broader goal is to develop mathematically grounded learning methods that remain interpretable, robust, and useful in scientific applications. This includes principled methodology for heterogeneous data, Bayesian inference, uncertainty-aware prediction, and statistical learning for biological systems.

Current appointment

  • Institution: Queensland University of Technology
  • Unit: School of Mathematical Sciences
  • Centre: ARC Centre of Excellence for the Mathematical Analysis of Cellular Systems (MACSYS); QUT Centre for Data Science
  • Research program: MACSYS
  • Position: Postdoctoral Research Fellow / Lecturer

Education

  • PhD: Statistics and Data Science, Normandie Université
  • MSc: Applied Mathematics, Université d’Orléans
  • BSc: Probability and Statistics, VNU-HCM University of Science

Research interests

  • Mathematical statistics and learning theory
  • Mixture-of-experts models and structured heterogeneity
  • Bayesian computation and uncertainty quantification
  • AI for science, systems biology, and genomics

Research

My research program sits at the intersection of mathematical statistics, machine learning, and scientific applications. I aim to design methods that are theoretically justified, computationally scalable, and practically valuable.

Statistical theory

Model selection, oracle inequalities, penalisation, minimal penalties, missing-data methodology, and non-asymptotic analysis for heterogeneous and high-dimensional models.

Mixture-of-experts models

Approximation theory, parameter estimation, identifiability, scalable optimisation, sparse modelling, and uncertainty-aware inference for mixture-of-experts and related conditional mixture models.

Bayesian computation

Bayesian synthetic likelihood, surrogate posteriors, MCMC, variational methods, uncertainty quantification, and principled probabilistic inference under computational and modelling constraints.

AI for science

Applications in systems biology, genomics, transcriptomics, proteomics, and scientific machine learning, with a focus on interpretable and trustworthy methods for real scientific discovery.

Selected publications

View full publication catalogue

A selection of recent and representative publications across mathematical statistics, mixture-of-experts models, Bayesian computation, and AI for science.

2026 · Econometrics and Statistics

On the large-sample limits of some Bayesian model evaluation statistics

Hien Duy Nguyen; Mayetri Gupta; Jacob Westerhout; TrungTin Nguyen.

Open paper
2026 · ICLR 2026

FACET: A Fragment-Aware Conformer Ensemble Transformer

Duy Minh Ho Nguyen; Trung Quoc Nguyen; Ha Thi Hong Le; Mai Thanh Nhat Truong; TrungTin Nguyen; Nhat Ho; Khoa D. Doan; Duy Duong-Tran; Li Shen; Daniel Sonntag; James Zou; Mathias Niepert; Hyojin Kim; Jonathan E. Allen. ICLR 2026.

Open paper
2026 · AISTATS 2026 Spotlight

Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency Without Model Sweeps

Do Tien Hai; Trung Nguyen Mai; TrungTin Nguyen; Nhat Ho; Binh T Nguyen; Christopher Drovandi. AISTATS 2026 Spotlight.

Open paper
2025 · Bayesian Analysis

Revisiting Concentration Results for Approximate Bayesian Computation

Hien Duy Nguyen; TrungTin Nguyen; Julyan Arbel; Florence Forbes.

Open paper
2025 · NeurIPS 2025

LoGra-Med: Long Context Multi-Graph Alignment for Medical Vision-Language Model

Duy M. H. Nguyen; Nghiem T. Diep; Trung Q. Nguyen; Hoang-Bao Le; Tai Nguyen; Tien Nguyen; TrungTin Nguyen; Nhat Ho; Pengtao Xie; Roger Wattenhofer; James Zhou; Daniel Sonntag; Mathias Niepert. NeurIPS 2025.

Open paper
2025 · NeurIPS 2025

A Unified Framework for Variable Selection in Model-Based Clustering with Missing Not at Random

Binh H Ho; Long Nguyen Chi; TrungTin Nguyen; Binh T Nguyen; Van Ha Hoang; Christopher Drovandi. NeurIPS 2025.

Open paper
2024 · NeurIPS 2024

Accelerating Transformers with Spectrum-Preserving Token Merging

Hoai-Chau Tran; Duy MH Nguyen; Manh-Duy Nguyen; TrungTin Nguyen; Ngan Hoang Le; Pengtao Xie; Daniel Sonntag; James Zou; Binh T. Nguyen; Mathias Niepert. NeurIPS 2024.

Open paper
2024 · Journal of Nonparametric Statistics

Bayesian nonparametric mixture of experts for inverse problems

TrungTin Nguyen; Florence Forbes; Julyan Arbel; Hien Duy Nguyen.

Open paper
2024 · AISTATS 2024

Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts

Huy Nguyen; TrungTin Nguyen; Khai Nguyen; Nhat Ho. AISTATS 2024.

Open paper
2023 · NeurIPS 2023 Spotlight

Demystifying Softmax Gating Function in Gaussian Mixture of Experts

Huy Nguyen; TrungTin Nguyen; Nhat Ho. NeurIPS 2023 Spotlight.

Open paper
2023 · EMNLP 2023 Main

HyperRouter: Towards Efficient Training and Inference of Sparse Mixture of Experts

Truong Giang Do; Huy Khiem Le; Quang Pham; TrungTin Nguyen; Binh T. Nguyen; Thanh-Nam Doan; Chenghao Liu; Savitha Ramasamy; Xiaoli Li; Steven HOI. EMNLP 2023 Main.

Open paper
2022 · Electronic Journal of Statistics

A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models

TrungTin Nguyen; Hien Duy Nguyen; Faicel Chamroukhi; Florence Forbes.

Open paper
2022 · Statistics and Computing

Summary statistics and discrepancy measures for approximate Bayesian computation via surrogate posteriors

Florence Forbes; Hien Duy Nguyen; TrungTin Nguyen; Julyan Arbel.

Open paper
2021 · Journal of Statistical Distributions and Applications

Approximations of conditional probability density functions in Lebesgue spaces via mixture of experts models

Hien Duy Nguyen; TrungTin Nguyen; Faicel Chamroukhi; Geoffrey J. McLachlan.

Open paper

Featured collaborators

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I collaborate across statistics, machine learning, and scientific domains, particularly on methodology for mixture-of-experts models, Bayesian inference, and AI for science.

Christopher DrovandiQUT · Statistics Hien Duy NguyenLa Trobe University / Kyushu University Florence ForbesInria Grenoble Alpes Julyan ArbelInria Grenoble Alpes Faïcel ChamroukhiUniversité de Caen Normandie Geoff McLachlanThe University of Queensland Nhat HoThe University of Texas at Austin Xin GuoThe University of Queensland

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