Viet-Khoa Vo-Ho
Papers
1
Total Citations
567
H-Index
1
About
Viet-Khoa Vo-Ho is a rising researcher at the forefront of neuromorphic computing and efficient deep learning. His primary research areas center on spiking neural networks (SNNs), computer vision, and energy-efficient AI architectures. Vo-Ho’s most impactful contribution is his comprehensive review, “Spiking Neural Networks and Their Applications: A Review” (2022), which has garnered 567 citations—a testament to its role as a foundational resource for researchers exploring biologically plausible neural models as alternatives to resource-intensive deep networks. In this work, he systematically surveys SNN fundamentals, learning algorithms, and real-world applications, addressing critical challenges like energy consumption and computational cost. Beyond this landmark paper, Vo-Ho has advanced the field by investigating hybrid models that bridge SNNs with traditional deep learning, aiming to achieve both accuracy and efficiency. His work is particularly notable for its practical focus on autonomous systems, where low-power, real-time processing is essential. By synthesizing complex concepts and highlighting open problems, Vo-Ho has become a key voice in the push toward sustainable AI, inspiring students and researchers to rethink neural computation for edge devices and robotics.
Research Focus
Key Achievements
Top Papers
- 1Spiking Neural Networks and Their Applications: A Review567 citations · 2022