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About

Dimitrios Makris is a researcher working at the intersection of neuromorphic computing, computer vision, and autonomous robotics. His work focuses on leveraging event-based sensing technologies to overcome persistent challenges in real-world robotic systems, including motion blur, low-light conditions, occlusion, and the demands of real-time operation. A central contribution of his research is the development of novel data augmentation techniques for neuromorphic vision sensors, addressing the critical scarcity of event-based datasets in robotics — work that has garnered 13 citations since its publication in 2022. Makris has also pioneered innovative segmentation architectures, most notably Bimodal SegNet, which fuses event-camera data with conventional RGB frames to achieve robust instance segmentation for robotic grasping, accumulating 14 citations across related publications. His 2023 Graph Mixer Neural Network further advances panoptic segmentation under dynamic conditions. Earlier work in cognitive robotic control demonstrates his broader interest in adaptive, intelligent systems, incorporating particle swarm optimization and entropy-based fitness quantification. Across his portfolio, Makris consistently bridges the gap between emerging sensor technologies and practical robotics applications, making his research highly relevant to students and practitioners pursuing next-generation autonomous systems.