Abhinav Kumar
Papers
1
Total Citations
12
H-Index
1
About
Abhinav Kumar is an emerging researcher at the intersection of machine learning, IoT systems, and industrial automation. His work focuses on developing computationally efficient and data-efficient learning frameworks tailored for resource-constrained environments, particularly in the context of smart manufacturing and industrial IoT deployments. Kumar's most notable contribution, "A Label-Efficient Semi Self-Supervised Learning Framework for IoT Devices in Industrial Process" (2023), addresses a critical bottleneck in deploying deep learning models on assembly and disassembly lines — the prohibitive cost of labeled data and high computational demands. By innovatively combining semi-supervised and self-supervised learning paradigms, his framework significantly reduces the dependency on large annotated datasets while maintaining strong representational performance on edge devices. This work has already garnered 12 citations since publication, signaling meaningful early impact within the industrial AI and embedded intelligence communities. Kumar's research is particularly relevant to researchers and practitioners seeking scalable, real-world solutions for intelligent automation, positioning him as a promising contributor to the growing field of edge intelligence and label-efficient learning in industrial settings.
Research Focus
Key Achievements
Top Papers
- 1