Biography
Sara Fridovich-Keil is an assistant professor in the School of Electrical and Computer Engineering, and program faculty in machine learning. Before joining Georgia Tech, Fridovich-Keil was a postdoc at Stanford and completed her Ph.D. at UC Berkeley in 2023 and her B.S.E. at Princeton in 2018. Her research focuses on foundations and applications of machine learning and signal processing in computational imaging.
Research
- Computational imaging
- Foundations of machine learning
- Signal processing
- Optimization and reconstruction algorithms
- Computed tomography
- 3D reconstruction
Distinctions & Awards
- NSF Mathematical Sciences Postdoctoral Research Fellowship, 2023
- IEEE Signal Processing Society Computational Imaging Technical Committee Member, 2025-2027
- UC Berkeley Demetri Angelakos Memorial Achievement Award, 2022
Publications
- Plenoxels: Radiance Fields Without Neural Networks
- K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
- Geometric Algebra Planes: Convex Implicit Neural Volumes
- Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains
- Gradient Descent Provably Solves Nonlinear Tomographic Reconstruction
- Accurate, Provable, and Fast Nonlinear Tomographic Reconstruction: A Variational Inequality Approach