Abolfazl Razi

Clemson University, Northern Arizona University

Papers

Total Citations

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About

Abolfazl Razi is a leading researcher at the intersection of machine learning, wireless communications, and multi-agent systems. His work is distinguished by its focus on creating intelligent, decentralized networks and robotic systems that can operate autonomously in dynamic environments. A major contribution is his pioneering work on predictive routing for wireless networks, where he developed robotics-based testbeds to validate protocols for the highly mobile nodes characteristic of the Internet of Things (IoT). His most cited paper, a comprehensive 2023 review on electronic nose (E-nose) design, has garnered 39 citations, highlighting his broad impact in translating biological olfactory principles into robotic sensing applications. More recently, Razi has pushed the boundaries of multi-agent learning with his innovative "Turbo-IRL" framework. This work, which draws inspiration from turbo decoding to solve inverse reinforcement learning problems in parallel, enables swarms of agents to efficiently infer a shared reward function. By bridging communication theory and artificial intelligence, Razi is crafting the foundational algorithms for the next generation of autonomous, collaborative robotic systems.