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About

Ziqi Zhang is a pioneering researcher at the intersection of computer vision, human-robot interaction, and swarm intelligence. Their work spans adversarial machine learning, affective computing for service robotics, and self-organizing multi-robot systems. Zhang’s most influential paper, "Adversarial Attacks on Monocular Depth Estimation" (2020, 14 citations), reveals critical vulnerabilities in deep neural networks for depth perception, highlighting security risks for real-world deployment. In a groundbreaking contribution to retail robotics, Zhang developed the "Consumer Shopping Emotion and Interest Database" (2022, 8 citations)—a unique multimodal dataset enabling service robots to infer consumer emotions and shopping intentions with accuracy surpassing human sales associates. This work directly addresses the challenge of imbuing robots with empathy. Zhang also advanced swarm robotics through the "Dynamic Response Threshold Model" (2023, 3 citations), which enables foraging robots to self-organize task allocation adaptively in changing environments. Most recently, their research on shared control via hybrid brain-computer interfaces (2024, 2 citations) pushes boundaries in assistive robotics. With a growing citation footprint and contributions spanning adversarial robustness, affective AI, and swarm coordination, Zhang is shaping the future of intelligent, empathetic, and secure robotic systems.