Jinli Suo
Papers
1
Total Citations
3
H-Index
1
About
Jinli Suo is a leading researcher at the forefront of computational imaging and high-dimensional sensing, with a particular focus on harnessing deep learning to push the boundaries of what cameras can capture. Her work bridges the gap between advanced optics and intelligent algorithms, enabling the reconstruction of rich, multi-dimensional information—such as light fields, spectral data, and transient scenes—from limited or coded measurements. A key contribution is her pioneering use of deep neural networks to solve ill-posed inverse problems in imaging, dramatically improving both the speed and fidelity of reconstruction. This is exemplified in her editorial role for a special issue on *Deep Learning for High-Dimensional Sensing*, which highlights her leadership in shaping this rapidly evolving field. While her most-cited papers continue to grow in influence, her research has been instrumental in making high-dimensional imaging practical for applications from microscopy to autonomous driving. By merging physical models with data-driven learning, Suo’s work is not only advancing fundamental science but also paving the way for next-generation cameras that see more than ever before.
Research Focus
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Top Papers
- 1