Fundamental AI Research
Advancing the foundations of Artificial Intelligence through safety, trustworthiness, human–AI ecosystems, and multi-agent autonomy — exploring how intelligent systems learn, collaborate, and evolve over time.
AI Safety & Trustworthiness
We develop methods to ensure AI systems are reliable, transparent, and equitable — addressing bias, uncertainty, robustness to real-world clinical variation, security vulnerabilities, demographic leakage, and safe use of generative AI in radiology and beyond.
Human–AI Ecosystem
We investigate how AI systems can collaborate with each other and with humans — learning across sites, agents, and tasks. This includes the development of SheLL (Shared Experience Lifelong Learning), multi-agent reasoning, and foundations for autonomous research workflows.