Haipeng Yu
Papers
1
Total Citations
2
H-Index
1
About
Haipeng Yu is an emerging computational neuroscience and artificial intelligence researcher whose work sits at the intersection of behavioral modeling and deep learning. His most notable contribution to date is the development of PursuitNet, a sophisticated deep learning architecture designed to predict and simulate competitive pursuit-like behavior in mice. Published in 2025, this work addresses the complex challenge of modeling dynamic predator-prey interactions — a problem that demands capturing abrupt, real-time behavioral shifts that traditional trajectory prediction models struggle to replicate. To support this research, Yu contributed the PEC dataset, a specialized resource documenting real-time pursuit-escape behavioral sequences with high temporal resolution. Crucially, PursuitNet demonstrates measurable superiority over established trajectory prediction frameworks including Social GAN and TUTR, establishing it as a meaningful methodological advance in the field. Though early in his citation trajectory with 2 citations, Yu's research opens promising directions for computational ethology, autonomous agent modeling, and neuroscience-informed AI. His integration of biological behavior with predictive modeling reflects a growing and important trend in building AI systems grounded in real-world animal dynamics.
Research Focus
Key Achievements
Top Papers
- 1