Anthony Bisulco

University of Pennsylvania, Samsung (United States)

Papers

Total Citations

H-Index

About

Anthony Bisulco is a leading researcher at the intersection of event-based vision and high-speed robotics, whose work is redefining how machines perceive and react in dynamic, low-latency environments. His primary research areas include multi-sensor fusion, neuromorphic sensing, and real-time robotic perception. Bisulco’s most influential contribution is **M3ED**, the first multi-robot, multi-sensor, multi-environment event dataset, which has garnered **50 citations** since its 2023 publication. This foundational resource provides synchronized event-camera data from ground, legged, and aerial robots in challenging conditions, enabling the community to benchmark and advance high-speed perception. His work **EV-Catcher** (24 citations) demonstrates the practical power of event-based neural networks for object catching in dynamic settings, showcasing latency reductions critical for real-time interaction. Most recently, his 2025 paper **EV-TTC** tackles dense time-to-collision estimation under low light, addressing a key gap in resource-constrained robotics. Through these contributions, Bisulco has established himself as a pioneer in making event cameras viable for agile, autonomous systems, with his datasets and algorithms serving as essential tools for researchers pushing the boundaries of robotic agility and safety.