Teresa Serrano‐Gotarredona
Papers
7
Total Citations
422
H-Index
6
About
Teresa Serrano‐Gotarredona is a pioneer in neuromorphic engineering, specializing in event-driven vision sensors and spike-based neural computation. Her groundbreaking work centers on Dynamic Vision Sensors (DVS), which mimic biological retinas by asynchronously detecting pixel-level brightness changes—enabling microsecond latency, high dynamic range, and ultra-low power consumption. Her most cited paper (320 citations) presents a 128×128 DVS achieving 1.5% contrast sensitivity and 4 mW power, revolutionizing real-time vision for robotics and embedded systems. She has also advanced spike-based visual recognition through benchmarking datasets and algorithms, bridging neuromorphic sensing with spiking neural networks (SNNs). Her contributions extend to high-speed robotic vision, demonstrating event-driven processing for rapid motor control, and to multi-foveated DVS designs that dynamically reconfigure resolution—a novel approach for efficient scene analysis. With over 400 cumulative citations, Serrano‐Gotarredona’s work has enabled energy-efficient, low-latency vision systems for autonomous navigation, industrial inspection, and brain-inspired computing. Her achievements include leading the development of the first fully asynchronous DVS with integrated preamplifiers and pioneering methods for downscaling event data, making her a key figure in neuromorphic hardware and real-world AI applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2Benchmarking Spike-Based Visual Recognition: A Dataset and Evaluation37 citations · 2016
- 3Event-driven sensing and processing for high-speed robotic vision31 citations · 2014
- 4Event Data Downscaling for Embedded Computer Vision13 citations · 2022
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