Papers
5
Total Citations
74
H-Index
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About
Malu Zhang is a leading researcher at the intersection of neuromorphic computing, robotic audition, and biologically inspired artificial intelligence. His primary research focuses on developing brain-inspired computational models for sound source localization (SSL), leveraging spiking neural networks (SNNs) to emulate the remarkable auditory processing capabilities of mammals. Zhang’s most influential work, "Multi-Tone Phase Coding of Interaural Time Difference for Sound Source Localization With Spiking Neural Networks" (2021, 33 citations), introduces a novel SNN-based framework that achieves precise and robust SSL in complex acoustic environments by encoding interaural time differences through multi-tone phase coding. This work, along with his earlier foundational paper "Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network" (2020), demonstrates the practical deployment of these algorithms on real-time robotic systems. Zhang’s contributions extend to human-robot interaction, as seen in his "HuRAI" model (2021, 12 citations), and to energy-efficient learning with his recent work on spike-based deep reinforcement learning (2025, 6 citations). His research has garnered over 70 citations, establishing him as a key innovator in creating low-power, biologically plausible solutions for robotic perception and autonomous decision-making.
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