We study and build robust, private, and scalable machine learning systems.
SAIL focuses on principled AI systems that can be trusted, distributed across real-world settings, and made efficient enough for practical deployment.
We study how machine learning systems behave under uncertainty, distribution shifts, adversarial conditions, and privacy constraints. Our work focuses on robustness, privacy protection, backdoor attacks and defenses, trustworthy evaluation, and safer deployment.
We design learning systems that work across distributed clients, data silos, institutions, and edge devices without centralizing private data. Our work includes federated learning, personalized learning, federated unlearning, fairness, communication efficiency, and cross-silo collaboration.
We build efficient AI systems that reduce computation, communication, memory, and deployment cost. Our work studies resource-constrained learning, edge AI, efficient training, lightweight architectures, low-rank methods, and green AI infrastructure.
A short editorial strip for major lab updates without turning the homepage into an archive.
Prof. Kok-Seng Wong has been recognized as an ICML 2026 Gold Reviewer, and Thinh Nguyen has been recognized as an ICML 2026 Silver Reviewer.
Prof. Kok-Seng Wong will serve as an Area Chair for NeurIPS 2026.
TrustFed is funded by the Accelerating Research Excellence Program, VinUniversity, from April 2026 to April 2028. Principal Investigator: Prof. Kok-Seng Wong.
BackFed has been accepted at the ICLR 2026 Workshop on Principled Design for Trustworthy AI.
A current project connecting distributed learning, trustworthy AI, and scalable large-model collaboration.
A research project on trustworthy federated learning for large language models, focusing on robustness, privacy, evaluation, and scalable collaboration.
Funded by the Accelerating Research Excellence Program, VinUniversity. Principal Investigator: Prof. Kok-Seng Wong.
Timeline: 2026–2028