Learning adaptive sensing and active learning

Abstract

This thesis consists of two parts. Part 1 develops reinforcement learning methods for adaptive sensing in MRI acceleration, enabling patient-specific scanning strategies that reduce scan times. Part 2 explores machine learning approaches for active learning, where models guide their own training by selecting which examples to learn from. Additional work addresses simulator optimization through policy-guided surrogate model training.

Tim Bakker

Senior machine learning researcher

My current research interests include AI safety, LLM reasoning, reinforcement learning.