I'm a Ph.D. student at The Ohio State University advised by Wei-Lun (Harry) Chao .
My research investigates data and algorithmic bottlenecks that prevent autonomous driving systems from scaling to anywhere, anytime, and anyone .
These days, I've been mostly excited about data. Does the way we collect/annotate/use self-driving data really scale toward driving anywhere? For example, should we keep putting tremendous effort into annotating 3D bounding boxes for perception? Can we do something differently? My recent work explores auto-labeling with local expert agents, such as robotaxis (ICLR 2025) and roadside units (ECCV 2026). I believe there should be more scalable ways to expand self-driving all around the world.
I also work on LiDAR-guided stereo depth (IROS 2026), autoregressive 3D detectors (CVPR 2026 Findings), and data generation for multi-agent collaborative perception (CVPR 2025).
I'm interning at Waymo and have previously interned at LG AI Research .
I'm an outstanding reviewer for ECCV (2022, 2024) and CVPR (2026). My ECCV 2026 work also received the Best Poster Award at the CVPR 2026 DriveX Workshop .