Sara Fridovich-Keil

Research Abstract:

My current research focus is at the intersection of signal processing, optimization, and machine learning, particularly for solving inverse problems in computer vision as well as medical and scientific imaging. My research in inverse problems includes both applied and theoretical aims to improve the quality, computational efficiency, interpretability, and reliability of reconstruction methods. I am also interested in improving our understanding of how neural networks work, so that they can be made more robust to distribution shifts between training and test data.

Bio:

Sara Fridovich-Keil is a postdoctoral scholar at Stanford University, where she works with Mert Pilanci and Gordon Wetzstein on foundations and applications of machine learning and signal processing in computational imaging. She is currently supported by an NSF Mathematical Sciences Postdoctoral Research Fellowship. Sara received her PhD in electrical engineering and computer sciences in May 2023 from UC Berkeley, where she was advised by Ben Recht and supported by an NSF GRFP fellowship. Sara received her BSE in electrical engineering from Princeton University in 2018, where she was advised by Peter Ramadge and supported, in part, by a Barry Goldwater Scholarship. During her time at UC Berkeley, Sara also worked as a student researcher at Google Brain and collaborated with researchers at Lawrence Livermore National Lab, the University of Southern California, and UC San Diego.