Ziv Goldfeld joined the School of Electrical and Computer Engineering at Cornell University as an Assistant Professor in July 2019. He is a graduate field member of Computer Science, Statistics and Data Science, Operations Research and Information Engineering, and the Center of Applied Mathematics at Cornell University. He is also a member of the Foundations of Information, Networks, and Decision Systems (FIND) group. During the 2017-2019 academic years, he was a postdoctoral research fellow in LIDS of the Electrical Engineering and Computer Science Department at MIT. Before that, Goldfeld received his B.Sc., M.Sc. (both summa cum laude) and Ph.D. in the Department of Electrical and Computer Engineering at Ben Gurion University of the Negev. Honors include the NSF CAREER Award, the IBM University Award, the Michael Tien ’72 Excellence in Teaching Award, and the Rothschild postdoctoral fellowship.
Goldfeld's research aims to develop theoretical foundations for learning algorithms that are provably accurate, efficient, scalable, robust, and private. To model a variety of learning tasks holistically, he employs a flexible mathematical framework based on statistical divergences and information measures. Through this lens, many learning tasks can be abstractly viewed as operations in a suitable geometry on probability distributions over high-dimensional manifolds. Goldfeld studies fundamental aspects of learning and inference from this perspective, integrating ideas from optimal transport theory, information theory, mathematical statistics, optimization, and applied probability. This interdisciplinary approach often reveals new connections that foster cross-field fertilization.
B.Sc. Electrical and Computer Engineering, Ben Gurion University of the Negev, Israel, 2012
M.Sc. Electrical and Computer Engineering, Ben Gurion University of the Negev, Israel, 2012
Ph.D. Electrical and Computer Engineering, Ben Gurion University of the Negev, Israel, 2018
Postdoctoral fellow, Laboratory for Information and Decision Systems (LIDS), Electrical Engineering and Computer Science, MIT, 2017-2019
