Artemis Panagopoulou
Papers
1
Total Citations
14
H-Index
1
About
Artemis Panagopoulou is a rising researcher at the intersection of neuromorphic computing and robotic vision, whose work is redefining how machines perceive motion in real time. Her primary research areas include spiking neural networks (SNNs), event-based vision, and optical flow estimation for autonomous systems. Her most cited paper, "Self-Supervised Optical Flow with Spiking Neural Networks and Event Based Cameras" (2021, 14 citations), introduces a novel self-supervised framework that leverages the asynchronous, low-latency output of event cameras to compute optical flow directly from spike trains. This work is a significant contribution to the field, as it enables obstacle detection in highly dynamic environments—such as drones or agile robots—without the need for labeled data, addressing a critical bottleneck in SNN training. By combining the energy efficiency of neuromorphic hardware with the temporal precision of event sensors, Panagopoulou’s approach offers a path toward truly low-latency, low-power robotic perception. Her research stands out for its practical focus on real-world deployment, and she is recognized as a promising voice in the next generation of bio-inspired computer vision researchers.
Research Focus
Key Achievements
Top Papers
- 1