About

Haiyan Wu is a cognitive and computational neuroscientist whose research sits at the intersection of multisensory perception, selective attention, and neurorobotics. Her work focuses on understanding how the brain integrates and resolves conflicting information across sensory modalities — particularly in audiovisual contexts — and translating these insights into intelligent computational systems. Wu's most influential contribution, a 2020 review paper garnering 29 citations, systematically bridges decades of human selective attention research with modern computational modeling, offering a comprehensive framework spanning both unimodal and crossmodal perspectives. This work has become a valuable reference point for researchers designing attention-aware AI systems. Complementing this, her earlier computational models of crossmodal conflict resolution (2017, 10 citations) and corresponding neurorobotic experiments using the iCub platform (2018, 7 citations) demonstrate how biologically inspired mechanisms can enable robots to exhibit human-like perceptual robustness in complex, noisy environments. Beyond perception modeling, Wu has explored practical applications including human-robot collaboration safety in virtual reality and functional monitoring systems for industrial robots. Her interdisciplinary portfolio — spanning cognitive science, computational modeling, and robotics — reflects a commitment to grounding artificial perception in biological principles, making her work highly relevant to researchers in cognitive science, AI, and human-robot interaction.

Research Focus

Key Achievements

3
H-Index
6
Papers
53
Total Citations
9
Avg Citations/Paper
🏆 Most Cited Paper
What Can Computational Models Learn From Human Selective Attention? A Review From an Audiovisual Unimodal and Crossmodal Perspective
29 citations · 2020
📈 Most Prolific Year: 2018 (4 Papers)
🤝 Key Collaborators: 19
🏛 Institutions: University of Chinese Academy of Sciences, Chinese Academy of Sciences, Danish Technological Institute, Technical University of Denmark

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
Content generated · 14 days ago