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About

Shih-Chii Liu is a pioneering researcher at the intersection of neuromorphic engineering, event-based sensing, and energy-efficient computing. Her work has profoundly shaped how machines perceive and process sensory information in real time, drawing inspiration from biological neural systems to build faster, more power-efficient hardware. Liu is perhaps best known for her contributions to dynamic vision sensors (DVS), culminating in the landmark 240×180 global shutter spatiotemporal vision sensor (2014), which has garnered over 1,000 citations and remains a foundational reference in event-based vision. This sensor's extraordinary 130 dB dynamic range and sub-millisecond latency have made it transformative for robotics and real-time tracking applications. Her subsequent development of color DAVIS sensors and combined frame-event tracking algorithms further extended this technology's practical reach. Beyond vision, Liu has made significant contributions to neuromorphic audio processing, including spike-based binaural sound localization systems and adaptive algorithms for robotic hearing. Her work on spiking neural network accelerators—including Minitaur and EdgeDRNN—demonstrates a sustained commitment to bridging biological inspiration with deployable hardware for edge inference and IoT applications. With numerous high-impact publications, Liu's research has helped establish event-driven, sparse computation as a credible paradigm for next-generation intelligent sensing systems.