Sara Fridovich-Keil joined the Georgia Tech School of Electrical and Computer Engineering as an assistant professor on June 1, 2025.  

She brings with her a research agenda that blends mathematical theory with real-world applications in imaging and machine learning. 

“As an Atlanta native, I couldn’t be more excited to pursue this work at Georgia Tech ECE,” Fridovich-Keil said. “The School and the digital signal processing group here are home to outstandingly talented and supportive faculty, and I am fortunate to call them my colleagues.” 

Arijit Raychowdhury, the Steve W. Chaddick School Chair in ECE, noted Fridovich-Keil's unique blend of mathematical rigor and practical innovation. 

“Dr. Fridovich-Keil brings a fresh perspective to ECE,” he said. “Her work will not only deepen our strengths in computational imaging but also spark new interdisciplinary collaborations. We’re delighted to welcome her.”  

Her research explores how computation can help model and interpret the world from incomplete or indirect observations, a challenge that sits at the heart of technologies like MRI, CT scans, and computer vision. 

This work centers on inverse problems, which involve reconstructing signals or images from incomplete and noisy data. These problems require a deep understanding of both theoretical principles and practical techniques to extract meaningful information from limited observations. 

Fridovich-Keil is interested in how to design models that are both expressive and computationally efficient, and how to make them robust to the kinds of variability that arise in real-world settings. 

“Computational imaging is about making the invisible visible, using algorithms and computation,” she said. “My work brings together foundations from signal processing, optimization, and machine learning to push the limits of what we can see.” 

Before coming to Georgia Tech, she was a postdoctoral researcher at Stanford University. Her research there, supported by the National Science Foundation’s (NSF) Mathematical Sciences Postdoctoral Research Fellowship, focused on computational imaging and the theoretical underpinnings of machine learning. 

She completed her Ph.D. in electrical engineering and computer sciences at the University of California, Berkeley. Her doctoral work, also supported by the NSF, laid the groundwork for her current interests in signal representation, nonlinear compressive sensing, and principled priors and algorithms for computational imaging. 

In her free time, Fridovich-Keil enjoys hiking, gardening, painting, cooking, and reading fantasy and historical fiction.