Professor Pierce Elected Eastman Visiting Professor at Oxford
Niles A. Pierce, Professor of Applied & Computational Mathematics and Bioengineering, has been elected to the 74th Eastman Visiting Professorship at the University of Oxford. Professor Pierce is working to engineer molecular instruments capable of reading out and regulating the state of endogenous biological circuitry from within intact organisms. The Eastman Professorship is one of the world's most respected visiting professorships, bringing a distinguished American scholar to Oxford each year. It was established in 1929 from an endowment established by George Eastman, the founder of the Eastman Kodak Company. The Eastman Professorship has previously been held by four Caltech professors: Linus Pauling (1948), George Beadle (1958-59), J.F. Bonner (1963-64), and Harry Gray (1997-98).
Richard Murray Named to DOD Panel on Innovation
Richard M. Murray, Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering, has been named to the Defense Innovation Advisory Board by Secretary of Defense Ash Carter. Professor Murray joins 14 other scholars and innovators who will focus on new technologies and organizational behavior and culture. Secretary Carter has asked them to identify technology and practices from the private sector that could be used by the Department of Defense (DOD). [Caltech story]
Professor Yue Receives Bloomberg Data Science Grant
Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, is a recipient of the Bloomberg Data Science Research Grant Program. The program aims to support cutting-edge research in the broad field of machine learning, including specific areas such as natural language processing, information retrieval, machine-translation and deep neural networks. Professor Yue has proposed to study an alternative notion of interpretability, which he calls “dynamic interpretability”. The goal of dynamically interpretable models is to make predictions that are interpretable, rather than have the model itself be explicitly interpretable. With this alternative goal, one can circumvent much of the inherent tension between accuracy and traditional “static” interpretability, and move one step closer to interpretable production-strength models.[Bloomberg release]