With rapid progress in our ability to acquire, process, and learn from data, the true democratization of data-driven intelligence has never seemed closer. Unfortunately, there is a catch. Machine learning algorithms have traditionally been designed independently of the systems that acquire data. As a result, there is a fundamental disconnect between their promise and their real-world applicability. An urgent need has therefore emerged for integrating the design of learning and acquisition systems. 

In this talk, I will present an approach for addressing this learning-acquisition disconnect using interactive machine learning methods. In particular, I will consider the problem of learning graphical model structure in high dimensions. This will highlight how traditional (open loop) methods do not take into account data acquisition constraints that arise in applications ranging from sensor networks to calcium imaging of the brain. I will then demonstrate how one can close this loop using techniques from interactive machine learning.