Research in AI and machine learning spans core algorithmic foundations, large-scale models, data-centric approaches, and interdisciplinary applications. Emphasis is placed on building trustworthy, explainable, and robust AI systems that operate reliably in safety-critical domains such as healthcare, finance, and autonomous systems. The department also advances AI hardware and novel training paradigms, including federated and decentralized learning.

Representative topics include:

• Large language models
• Trustworthy and explainable AI
• Federated learning
• AI hardware
• Data-centric and safe AI
• AI for health, finance, and science