Trustworthy machine learning requires models that still work when real-world data changes, and Adam Klivans, Ph.D., Director of the Institute for Foundations of Machine Learning (IFML), emphasizes learning under “distribution shift” as a major barrier to relying on models to predict disease across different patient populations. IFML focuses on foundational algorithms and mathematical techniques that push generative AI forward, including better methods for training and inference in deep learning and advances in diffusion approaches. Klivans highlights robustness and safety as core priorities, asking how to trust a model trained in one setting when it is applied in another. IFML connects these foundations to use-inspired domains such as imaging, protein engineering and biologics, and AI for mathematical discovery. Series: "Data Science Channel" [Science] [Show ID: 40972]