Rahul Thapa
Papers
1
Total Citations
24
H-Index
1
About
Rahul Thapa is a researcher at the forefront of brain-inspired hyperdimensional computing (HDC), a paradigm that mimics neural processing through high-dimensional, holographic vectors. His work centers on developing memory-efficient machine learning solutions, with a particular focus on text spam detection and cybersecurity applications. Thapa’s most cited paper, "SpamHD: Memory-Efficient Text Spam Detection using Brain-Inspired Hyperdimensional Computing" (2021), has garnered 24 citations and demonstrates how hypervectors—pseudo-random, high-dimensional representations—can achieve remarkable accuracy while drastically reducing computational and memory overhead compared to traditional deep learning models. This contribution is especially significant for edge devices and resource-constrained environments. Thapa’s research bridges neuroscience and practical AI, showing how the mathematical properties of hypervectors align with brain function to enable robust, efficient classification. His work has implications for real-time spam filtering, anomaly detection, and beyond, positioning him as an emerging voice in the intersection of neuromorphic computing and applied machine learning.
Research Focus
Key Achievements
Top Papers
- 1