I develop a research program on human-like perception and learning through structure, efficiency, and continual adaptation rather than brute-force scale. I am on the 2026 faculty market.
Across projects, a unifying mechanism is analysis by synthesis: framing perception and learning as inverse problems where models infer latent structure by constructing and testing generative explanations.
My goal as an independent PI is to build models that connect mechanistic insight to practical systems for robust, reusable intelligence.
Problem: Models recognize objects but fail to reason over hierarchy, attributes, and affordances.
Key idea: A cognitively grounded taxonomy and evaluation of scene reasoning.
Outcome: Diagnostic signals that reveal where perception breaks and how structure helps.
Problem: Parts are segmented without semantics or named without structure.
Key idea: Joint part segmentation and naming as set alignment with scalable annotation.
Outcome: Reusable, consistent part semantics that scale under constrained data.
Problem: Perception lacks grounded 3D world models for reasoning and synthesis.
Key idea: Analysis by synthesis pipelines that reconstruct structured 3D worlds.
Outcome: World models that support part-level reasoning and compositional inference.
Problem: Continual adaptation is compute-heavy and prone to forgetting.
Key idea: Shared low-dimensional update geometry for reuse, merging, and strict continual learning.
Outcome: Sustainable adaptation that scales beyond privileged compute and data.