Sizhe Zhang

Papers

Total Citations

H-Index

About

Sizhe Zhang is a leading researcher in the emerging field of brain-inspired hyperdimensional computing (HDC), also known as Vector Symbolic Architecture (VSA). His work focuses on making HDC models robust against both cyber attacks and hardware errors, addressing critical challenges in deploying lightweight AI systems. Zhang’s most cited paper, “Robust Hyperdimensional Computing against Cyber Attacks and Hardware Errors” (2023, 8 citations), demonstrates how HDC can maintain reliability in adversarial environments—a key advantage over deep neural networks. His follow-up work, “ScaleHD” (2022, 7 citations), advances HDC’s scalability for real-world cognition tasks like robotics, bio-medical signal analysis, and natural language processing. In his 2025 study, “Exploring Hyperdimensional Computing Robustness Against Hardware Errors” (4 citations), Zhang further investigates HDC’s resilience to physical faults, reinforcing its potential for edge computing and medical diagnostics. By proving that HDC offers smaller model sizes, reduced computation, and one-shot learning while withstanding attacks and errors, Zhang is paving the way for safer, more efficient AI systems. His contributions are vital for students and researchers interested in neuromorphic computing, hardware security, and trustworthy machine learning.