Paul G. Allen School of Computer Science & Engineering
University of Washington
Jamieson’s research explores how to leverage already-collected data to inform what future measurements to make next, in a closed loop. Such active learning can extract considerably richer insights than any measurement plan fixed in advance, using the same statistical budget. His work ranges from theory to practical algorithms with guarantees to open-source machine learning systems and has been adopted in a range of applications, including measuring human perception in psychology studies, adaptive A/B/n testing in dynamic web-environments, numerical optimization, and choosing hyperparameters for deep neural networks.