Jeff Zhang

Harvard University, Arizona State University, Harvard University Press

Papers

Total Citations

H-Index

About

Jeff Zhang’s research sits at the intersection of robotics, wireless communications, and artificial intelligence, with a focus on enabling intelligent systems to navigate and interact with the physical world using millimeter-wave (mmWave) signals. His most impactful work, “Millimeter Wave Wireless Assisted Robot Navigation With Link State Classification” (2022, 20 citations), pioneers a method for high-precision robot localization by exploiting mmWave’s angular and temporal resolution, demonstrating how wireless signals can serve as a robust sensing modality in cluttered environments. Zhang further advanced this area in “Path Planning Under Uncertainty to Localize mmWave Sources” (2023, 4 citations), where he developed an Extended Kalman filter-based algorithm that allows mobile robots to efficiently locate wireless sources while navigating uncertain indoor spaces. More recently, his work “CLAIRE: Composable Chiplet Libraries for AI Inference” (2025, 4 citations) tackles the computational demands of large-scale AI models like GPT-4 by proposing modular chiplet architectures, addressing the physical limits of monolithic chip design. With a growing citation footprint, Zhang’s contributions are shaping how robots perceive their environment through wireless signals and how AI hardware can scale efficiently, making his research essential for students and engineers working on autonomous navigation, 6G localization, and energy-efficient AI systems.