* Uncertainty quantification, Probabilistic machine learning
* Supervised, unsupervised and semi-supervised learning
* Learning from imperfect data, Learning from mixed data
* Active learning, Ensemble learning

List of publications

*Google Scholar
*DBLP

Co-authors (a non-exhaustive list in alphabetical order)

*Yonatan Carlos Carranza Alarcón, Cassio de Campos, Sébastien Destercke, Johannes Fürnkranz, Cyprien Gilet, Xuan-Truong Hoang, Van-Nam Huynh, Eyke Hüllermeier, Mylène Masson, Eneldo Loza Mencía, Toan Nguyen-Mau, Michael Rapp, Mohammad Hossein Shaker, Yang Yang, Haifei Zhang.

Some publications (illustrating types of research questions I have been tackling)

* Nguyen, V. L., Zhang, H., & Destercke, S. (2024). Credal ensembling in multi-class classification (Short version). Machine Learning, ???(?), pp. 1-64.

* Nguyen, V. L., Yang, Y., & de Campos, Cassio P. (2023). Probabilistic Multi-Dimensional Classification. In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI), pp. 1522-1533.

* Nguyen, V. L., Shaker, M. H., & Hüllermeier, E. (2022). How to measure uncertainty in uncertainty sampling for active learning. Machine Learning, 111(1), 89-122.

* Nguyen, V. L., & Hüllermeier, E. (2021). Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence. Journal of Artificial Intelligence Research, 72, 613-665.

* Nguyen, V. L., Destercke, S., Masson, M. H., & Hüllermeier, E. (2018). Reliable multi-class classification based on pairwise epistemic and aleatoric uncertainty. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI), pp. 5089-5095.

* Nguyen, V. L., Destercke, S., & Masson, M. H. (2017). Querying partially labelled data to improve a K-nn classifier. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI), pp. 2401-2407.

In progress

* Kim-Dung Tran (co-supervised with Sébastien Destercke): Tensor decompositions and their applications in machine learning

In progress

* Chenrui Zhu (co-supervised with Mylène Masson and Sébastien Destercke): Robustness in Machine Learning Explanations

* Salvador Madrigal Castillo (co-supervised with Cyprien Gilet and Sébastien Destercke): “Minimax Classifiers for Multi-Label Classification”

* Thu-Ha Do (co-supervised with Yves Grandvalet): Probabilistic Graphical Models for Complex Learning Tasks

Defended

* No

In progress

* No

Defended

* Salvador Madrigal Castillo (co-supervised with Cyprien Gilet and Sébastien Destercke): “Minimax Classifiers for Multi-Label Classification”, University of Technology of Compiègne, France, 2024.

* Yang Yang (co-supervised with Cassio de Campos): Generalized Bayesian Network Classifiers, Eindhoven University of Technology, the Netherlands, 2022.

Invited Talks

* LFA (2024): Uncertainty modeling/quantification and its applications in machine learning

Conference Program committees

* ICML (2025)
* NeurIPS (2024)
* ICLR (2025)
* AAAI (2021, 2023)
* AISTATS (2021-2022)
* UAI (2021)
* ECAI (2024)
* ISIPTA (2023)
* IUKM (2023, 2025)

Conference/workshop organization committees

* SMPS/BELIEF (2018)
* WUML & WPMSIIP (2017)

Reviewing activities

* International Journal of Approximate Reasoning (2023, 2024)
* Pattern Recognition (2023, 2024)
* ACM Transactions on Probabilistic Machine Learning (2024)
* Journal of the American Statistical Association (2024)

Grant reviewing

* National Science Center, Poland (2024)