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 will be a Postdoctoral Research Fellow at the 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.

I am currently a Postdoctoral Research Fellow at the Inria centre at the University Grenoble Alpes in the Statify team, where I am very fortunate to be mentored by Florence Forbes, and Julyan Arbel and collaborate with Hien Duy Nguyen, 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).

I completed my Ph.D. Degree in Statistics and Data Science at Normandie Univ in December 2021, where I am 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, at the interface 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), audio processing (sound source localization), biostatistics (genomics, transcriptomics, proteomics), computer vision (image segmentation).