Jindi Zhang

About

Jindi Zhang is a rising leader in the field of multi-agent systems and artificial intelligence, with a focus on integrating Large Language Models (LLMs) into complex, real-world coordination problems. Her key research areas include heterogeneous multi-agent systems (HMAS), task coordination, and hybrid AI architectures. Zhang’s most notable contribution is the development of AutoHMA-LLM, a pioneering framework that synergizes LLMs with classical control methods to enable efficient task coordination and execution across diverse agents like drones, ground robots, and automated devices. This work, published in 2025, has already garnered 30 citations, reflecting its immediate impact on the robotics and AI communities. By bridging the gap between high-level reasoning and low-level control, Zhang’s research addresses critical challenges in scalability and adaptability for autonomous systems. Her innovative approach promises to advance applications in search-and-rescue, logistics, and smart infrastructure, positioning her as a key figure in the next generation of intelligent, collaborative robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   AutoHMA-LLM: Efficient Task Coordination and Execution in Heterogeneous Multi-Agent Systems Using Hybrid Large Language Models

   30 citations · 2025

Key Collaborators

Contact & Links