Chankyu Lee
Papers
1
Total Citations
14
H-Index
1
About
Chankyu Lee is a leading researcher at the intersection of neuromorphic computing and computer vision, with a primary focus on developing efficient, low-latency perception systems using spiking neural networks (SNNs) and event-based cameras. His most influential work, "Self-Supervised Optical Flow with Spiking Neural Networks and Event Based Cameras" (2021, 14 citations), pioneers a novel approach that leverages the inherent temporal precision of event-based sensors to compute optical flow without costly labeled data. This contribution is critical for robotic applications requiring real-time obstacle detection in highly dynamic environments, where traditional frame-based methods struggle with bandwidth and latency constraints. By demonstrating that SNNs can be trained in a self-supervised manner directly on event streams, Lee has opened new pathways for deploying energy-efficient, biologically inspired vision systems on resource-constrained platforms. His research directly addresses the challenge of bridging neuromorphic hardware with practical robotics, making him a notable figure in the push toward low-power, high-speed autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1