Biography

Hi there and welcome! My Vietnamese name is Nguyễn Trung Tín. I therefore used “TrungTin Nguyen” or “Trung Tin Nguyen” in my English publications. Also, the first name is “Tín” or “Tin” for short. I am currently a Postdoctoral Fellow at the Inria Grenoble-Rhône-Alpes in the Statify team where I am very fortunate to be mentored by Senior Researcher Florence Forbes, Senior Lecturer Hien Duy Nguyen, and Associate Researcher Julyan Arbel. I completed my Ph.D. Degree in Statistics and Data Science at Normandie Univ, UNICAEN, CNRS, LMNO, Caen, France in December 2021 where I am very fortunate to be advised by Professor Faicel Chamroukhi. During my Ph.D. research, I am also very fortunate to collaborate with Professor Geoff McLachlan focusing in mixture models. I received a Visiting PhD Fellowship at the Inria Grenoble-Rhône-Alpes Research Centre, working with Senior Researcher Florence Forbes and Associate Researcher Julyan Arbel in the Statify team under a project LANDER (from September 2020 to January 2021).

A central theme of my research focuses on Data Science, 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 such as Dirichlet process mixture models), high-dimensional statistics (variable selection and regularization such as Lasso, graphical models).
• Machine learning: Supervised learning (deep hierarchical mixtures of experts (MoE) such as polynomial regression and logistic regression, deep neural networks), unsupervised learning (clustering such as mixture models, dimensionality reduction such as principal component analysis, deep generative models such as variational autoencoders, generative adversarial networks and normalizing flow models), reinforcement learning (partially observable Markov decision process).
• Optimization: Robust and effective optimization algorithms for deep hierarchical MoE (expectation–maximization (EM) algorithm, variational Bayesian EM algorithm, online and mini-batch majorization-minimization (MM) algorithm), difference of convex algorithm, optimal transport (Wasserstein distance).
• Biostatistics: Statistical learning and machine learning for large biological data sets (genomics, transcriptomics, proteomics).