Papers
2
Total Citations
9
H-Index
2
About
Biao Han is a researcher at the forefront of distributed intelligence and efficient AI inference, whose work bridges the gap between advanced machine learning and resource-constrained real-world systems. His primary research areas encompass collaborative AI, multi-robot communication, and distributed computing. Han’s major contribution lies in pioneering frameworks that enable powerful models to operate beyond data-center boundaries. His most notable work, "CoLLM: A Collaborative LLM Inference Framework for Resource-Constrained Devices" (2024), tackles the critical challenge of deploying large language models on edge devices by orchestrating distributed computation, a breakthrough that has already garnered 7 citations shortly after publication. Earlier, he explored multi-robot cooperative communication in his 2020 paper, "Distributed Intelligence Empowered Data Aggregation and Distribution for Multi-robot Cooperative Communication," which addresses the limitations of single-hop protocols by introducing network-layer routing for robust multi-hop data sharing. This foundational work demonstrates his sustained focus on enabling collective intelligence in decentralized systems. With a growing citation impact, Han’s research is essential for students and engineers seeking to understand how AI can be made both powerful and practical for mobile, resource-limited platforms.
Research Focus
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Top Papers
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