Pankaj Velavan
Papers
1
Total Citations
4
H-Index
1
About
Pankaj Velavan is a rising researcher at the forefront of explainable artificial intelligence (XAI) and depth-sensing technologies. His work critically bridges the gap between complex AI decision-making and human interpretability, particularly within spatial computing environments. Velavan’s most cited study, "Depth Sensing in AI on Exploring the Nuances of Decision Maps for Explainability" (2024), has already garnered 4 citations, establishing a foundation for integrating depth data into transparent AI models. This research is pivotal for high-stakes fields like robotics and healthcare, where understanding an AI’s “why” is as crucial as its “what.” By focusing on decision maps derived from 3D sensor data, Velavan provides a novel framework that enhances model accountability and user trust. His contributions are shaping a new generation of interpretable autonomous systems, making him a notable voice in the ongoing quest for ethical and understandable AI. As his citation count grows, Velavan’s work promises to influence both academic research and practical deployment of intelligent machines.
Research Focus
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Top Papers
- 1