Welcome to the Security and Artificial Intelligence Lab
Our research group is situated within VinUniversity’s College of Engineering and Computer Science. We specialize in the field of trustworthy AI, with a core focus on simplifying the development and deployment of machine learning models while ensuring their robustness. Our research encompasses low-complexity generative approaches, strengthening algorithmic robustness, and tackling critical challenges in machine learning and federated learning to enhance security, privacy, efficiency, and fairness.
We are looking for passionate new PhD students, Postdocs, and Master students to join the team (more info) !
News
March, 2026
Our paper titled BackFed: A Standardized and Efficient Benchmark Framework for Backdoor Attacks in Federated Learning has been accepted at the ICLR 2026 Workshop on Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities.
February, 2026
Our paper titled HFedATM: Hierarchical Federated Domain Generalization via Optimal Transport and Regularized Mean Aggregation has been accepted at the main technical track of CVPR 2026.
February, 2026
Our papers titled Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning and Memory-efficient Continual Learning with Prototypical Exemplar Condensation have been accepted at the CVPR 2026 Findings.
November, 2025
Our paper titled Clean-Label Physical Backdoor Attacks with Data Distillation has been accepted at the main technical track of AAAI 2026.
September, 2025
Our paper titled Clean-Label Physical Backdoor Attacks with Data Distillation has been accepted at the Reliable ML from Unreliable Data workshop at NeurIPS 2025.