Sugandha Sharma
Papers
1
Total Citations
3
H-Index
1
About
Sugandha Sharma’s research lies at the intersection of cognitive science, computational neuroscience, and artificial intelligence, with a focus on how agents—both biological and artificial—learn and plan in complex environments. Her work centers on understanding the principles that govern spatial representation and decision-making, particularly how the brain fragments continuous spaces into discrete, manageable maps. In her most-cited paper, “Fragmented Spatial Maps from Surprisal: State Abstraction and Efficient Planning” (2021, 3 citations), Sharma introduces a novel framework that frames spatial fragmentation as an online clustering problem driven by surprisal—the unexpectedness of sensory events. This work offers a simple yet powerful predictive principle for where and why cognitive maps break apart, bridging theoretical insights with practical implications for efficient planning. By linking state abstraction to surprise, Sharma’s contributions illuminate how organisms simplify complex environments, with potential applications in robotics and AI for scalable navigation. Her research, though early in its citation trajectory, demonstrates a clear and innovative approach to foundational questions in spatial cognition, positioning her as a rising voice in computational cognitive science.
Research Focus
Key Achievements
Top Papers
- 1