Keng Teck
Papers
1
Total Citations
5
H-Index
1
About
Keng Teck is a researcher whose work centers on cognitive modeling and spatial intelligence, with a particular focus on egocentric spatial memory (ESM)—the ability to encode, store, and recall spatial information from a first-person perspective. His most-cited paper, "Egocentric Spatial Memory" (2018, 5 citations), introduces a pioneering deep neural network architecture that learns to estimate occupancy in an environment, bridging computational neuroscience and artificial intelligence. This contribution offers a novel framework for understanding how agents navigate and remember space, with implications for robotics, autonomous systems, and human-robot interaction. While his citation count is modest, the work stands out for its conceptual depth and interdisciplinary reach, laying groundwork for future studies in spatial cognition. Teck’s research is particularly notable for its integration of biological memory principles into machine learning models, offering a fresh perspective on how intelligent systems can perceive and interact with their surroundings. For students and researchers exploring the intersection of memory, perception, and AI, Teck’s work provides a compelling entry point into the challenges of building truly spatially aware agents.
Research Focus
Key Achievements
Top Papers
- 1Egocentric Spatial Memory5 citations · 2018