Aram Kawewong
Papers
13
Total Citations
285
H-Index
8
About
Aram Kawewong is a computer scientist whose research spans robotics, computer vision, and machine learning, with particular expertise in simultaneous localization and mapping (SLAM), incremental learning, and zero-shot classification. His most influential contribution is the development of Position-Invariant Robust Features (PIRFs), a novel framework for dynamic scene recognition that enables mobile robots to reliably navigate and build maps in environments where visual conditions change dramatically. His 2010 paper on online and incremental appearance-based SLAM has garnered 69 citations, demonstrating the real-world relevance of this work for autonomous systems operating in unpredictable settings. Kawewong further advanced the field through his pioneering work on online incremental zero-shot learning, addressing the practical challenge of classifying previously unseen object categories in robotics and mobile communications — a paper that has attracted over 105 citations, making it his most recognized contribution. His broader research program consistently emphasizes online and incremental approaches, reducing reliance on large offline datasets and enabling systems to adapt in real time. Through self-organizing incremental neural networks applied to robot navigation and path planning, he has helped lay groundwork for more adaptive, scalable autonomous systems. His cumulative body of work reflects a sustained commitment to bridging theoretical machine learning with practical robotic applications.
Research Focus
Key Achievements
Top Papers
- 1Online incremental attribute-based zero-shot learning105 citations · 2012
- 2Online and Incremental Appearance-based SLAM in Highly Dynamic Environments69 citations · 2010
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- 7Self-Organizing Incremental Associative Memory-Based Robot Navigation9 citations · 2012
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