I'm a Ph.D. student at Boston University advised by Wei-Lun (Harry) Chao.
My goal is to solve foundational data bottlenecks first, then unlock generalized AI solutions for downstream domains, ranging from autonomous driving and robotics to AI4Science (e.g., ecology and evolutionary biology).
More on autonomous driving
These days, I'm excited about investigating data bottlenecks that prevent autonomous driving systems from scaling to anywhere, anytime, and anyone.
Does the way we collect, annotate, and use self-driving data really scale toward driving anywhere? For example, should we keep putting tremendous effort into annotating 3D bounding boxes for perception? My recent work explores auto-labeling from other road observers, such as vehicles already running perception (ICLR 2025) and roadside units (ECCV 2026). More broadly, I'm interested in rethinking how the autonomous driving community approaches data (NeurIPS 2026 Position Paper).
I have previously interned at Waymo and LG AI Research.
I'm an (4x) outstanding reviewer for ECCV (2022, 2024, 2026) and CVPR (2026). My ECCV 2026 work also received the Best Poster Award at the CVPR 2026 DriveX Workshop.