Susan Chan

Papers

Total Citations

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About

Susan Chan is a leading researcher in computational imaging and high-speed 3D sensing, with a focus on pushing the boundaries of temporal resolution for real-world applications. Her most cited work, "High-speed 3D sensing via hybrid-mode imaging and guided upsampling" (2020, 88 citations), introduces a novel hybrid-mode imaging framework that combines single-photon avalanche diode (SPAD) arrays with guided upsampling techniques. This breakthrough enables rapid, high-fidelity 3D depth acquisition, directly addressing critical challenges in autonomous vehicle navigation, fluorescence lifetime microscopy, and industrial inspection. Chan’s contributions have significantly advanced the practical deployment of SPAD-based sensors, offering a path to overcome traditional trade-offs between speed, resolution, and noise. Her research is widely recognized for its impact on real-time imaging systems, with her work cited extensively in both optics and computer vision communities. Beyond her flagship paper, Chan has published influential studies on computational reconstruction algorithms and hybrid sensor design, earning her a reputation as a key innovator in high-speed 3D sensing. Her achievements underscore a commitment to bridging theoretical imaging models with tangible, high-performance hardware solutions.