Angus Fung
Papers
7
Total Citations
63
H-Index
5
About
Angus Fung is a robotics and computer vision researcher whose work sits at the intersection of autonomous systems, deep learning, and human-centered environments. His research focuses on enabling service robots to detect, track, and search for people in complex real-world settings — from cluttered disaster zones to hospitals, airports, and retail spaces. Fung's most cited work, "Using Deep Learning to Find Victims in Unknown Cluttered Urban Search and Rescue Environments" (2020, 24 citations), established his early contributions to life-saving robotics applications. He has since advanced the field through multimodal deep contrastive learning for robust people detection under occlusion and pose variation (2023, 15 citations), and pioneered the use of diffusion models for dynamic people tracking with LDTrack (2025, 9 citations). Notably, his work on hand-drawn map navigation using vision-language models opens intuitive new pathways for human-robot communication, while his zero-shot MLLM-Search framework demonstrates how multimodal large language models can power autonomous person-finding without prior knowledge. With over 60 cumulative citations and a research trajectory spanning search-and-rescue to everyday service robotics, Fung's work is making autonomous robots significantly more capable and practically deployable in human-centered environments.
Research Focus
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Top Papers
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