Interactive Learning for Closed-Loop Design of Experiments, Simulations and Expert Feedback

In most scientific domains including material physics, high energy physics and cosmology, we have control over the data collection procedure from multiple diverse sources. This inspires use of interactive machine learning algorithms that not only find input-output associations but also interact with the data generating process making intelligent decisions about what data to collect, when and how much. This talk will exemplify such interactive learning algorithms that can guide closed-loop design and integration of experiments, simulations as well as prior knowledge in the form of expert feedback. We will talk about uncertainty and model misspecification in context of interactive algorithms that use machine learning models ranging from linear, decision trees, gaussian processes to neural networks.

Aarti Singh is an Associate Professor in the Machine Learning Department within the School of Computer Science at Carnegie Mellon University. Her research focuses on designing principled and efficient sequential decision-making algorithms for autonomous as well as human-in-the-loop settings with applications to scientific domains. Her work is recognized by an NSF Career Award, a United States Air Force Young Investigator Award, A. Nico Habermann Junior Faculty Chair Award, Harold A. Peterson Best Dissertation Award, and four best student paper awards. Her service honors include serving as Program Chair for the International Conference on Machine Learning (ICML) 2020, Program Chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference, member of the National Academy of Sciences (NAS) committee on Applied and Theoretical Statistics, Associate Editor for IEEE Transactions on Information Theory and Action Editor for the Journal of Machine Learning Research.