Jared Coleman

Papers

Total Citations

H-Index

About

Jared Coleman is a rising researcher at the intersection of robotics, distributed systems, and artificial intelligence, with a focus on enabling autonomous coordination in dynamic, networked environments. His most influential work introduces a **Graph Convolutional Network-based scheduler** for distributing computation in the Internet of Robotic Things (IoRT)—a domain where traditional scheduling methods fail due to rapidly changing network topologies. This paper, his most cited with 9 citations, addresses a critical gap in deploying complex distributed applications across mobile robotic swarms. Coleman also contributes to fundamental problems in multi-robot coordination, including message delivery between robots with different speeds and cooperative delivery-to-safety tasks, where he develops provable strategies for time-constrained, adversarial settings. His work on searching for an oblivious moving target on a line further demonstrates his strength in algorithmic search and pursuit-evasion theory. By blending graph neural networks with classical robotics challenges, Coleman is carving out a niche that bridges theoretical guarantees and practical deployment—making his research particularly relevant for students and engineers working on resilient, decentralized robotic systems.