Max Robotics — Global Service Robot Directory + US Certification

About

Fariborz Baghaei Naeini is an emerging researcher whose work sits at the dynamic intersection of neuromorphic computing, computer vision, and robotics. His research focuses primarily on event-based sensing technologies and their application to robotic perception, particularly in the challenging domains of object segmentation and contact force measurement. Naeini has made notable contributions by addressing one of the field's most pressing limitations — the scarcity of event-based training data — through his development of novel augmentation techniques for neuromorphic vision sensors, work that has attracted 13 citations since its 2022 publication. His pioneering Bimodal SegNet architecture, which fuses event camera data with traditional RGB frames to achieve robust instance segmentation, has demonstrated meaningful impact with 12 citations, offering practical solutions to real-world robotic challenges including occlusion, low-light conditions, and motion blur. More recently, his Graph Mixer Neural Network advances panoptic segmentation using asynchronous event data, further expanding the toolkit available for intelligent robotic grasping systems. Collectively, Naeini's work represents a forward-looking research agenda that leverages the unique advantages of neuromorphic sensors — high dynamic range and low latency — to push the boundaries of robotic perception in complex, unstructured environments.