How can we accelerate scientific discovery when experiments are costly and uncertainty is high? From protein engineering to robotics, data efficiency is critical—but advances in lab automation and the rise of foundation models are creating rich new opportunities for intelligent exploration. In this talk, I’ll share recent work toward closing the loop between learning and experimentation, drawing on active learning, Bayesian optimization, and reinforcement learning. I’ll show how we can guide exploration in complex, high-dimensional spaces; how meta-learned generative priors enable rapid adaptation from simulation to reality; and how even foundation models can be adaptively steered at test time to reduce their epistemic uncertainty. I’ll conclude by highlighting key challenges and exciting opportunities for machine learning to drive optimization and discovery across science and engineering.