Nick Haber
Papers
2
Total Citations
105
H-Index
2
About
Nick Haber is a leading researcher at the intersection of artificial intelligence, cognitive science, and developmental psychology. His work focuses on building computational models that capture how humans—especially children—learn to reason about the physical world and solve complex, hierarchical problems. In his highly cited 2018 paper, "Flexible Neural Representation for Physics Prediction" (91 citations), Haber introduced a hierarchical particle-based object representation that enables AI systems to flexibly understand physical dynamics, mirroring the human capacity to grasp object interactions at multiple levels of detail. This work has been foundational in advancing intuitive physics in AI. More recently, Haber co-developed Parsel (2022), a framework that allows large language models to decompose complex programming tasks into manageable subproblems, significantly improving their algorithmic reasoning. By bridging insights from developmental psychology with cutting-edge machine learning, Haber’s research not only pushes the boundaries of AI but also deepens our understanding of human cognition. His contributions are shaping how we build machines that learn and think like people.
Research Focus
Key Achievements
Top Papers
- 1Flexible Neural Representation for Physics Prediction91 citations · 2018
- 2