I am a Research Scientist at NVIDIA Research Autonomous Vehicle Research Group, working with Marco Pavone. I graduated from MIT EECS in 2022, advised by Justin Solomon at Geometric Data Processing Group. I was also fortunate to collaborate with Michael Bronstein and Phillip Isola. Previously, I was a master student at University of California, San Diego. Prior to that, I received my BEng in Computer Science from Zhejiang University. I’ve received the Nvidia Fellowship (2020-2021) and the MIT EECS William A. Martin Master’s Thesis Award (2021).
My research lies in the intersection of computer vision, computer graphics, and robotics. My goal is to use machine learning to enable robot intelligence with minimal human supervision. I study how to design 3D learning systems which leverage geometry, appearance, and any other cues that are naturally available in sensory inputs. I am also broadly interested in eclectic applications on top of these systems.
Topics I currently focus on include:
- Neural fields for robotics and autonomous driving: FreeNeRF
- Generative AI for 3D perception
- Geometry-inspired self-supervised learning
- Geometric deep learning: DGCNN, DCP, PRNet, Object DGCNN, PointGrow
- 3D perception for robotics and autonomous driving: PillarOD, Object DGCNN, DETR3D, HDMapNet
- 2D/3D self-supervised learning and few-shot learning: FeatureDecor, RFS, STAR, RepLearnPoint
We are organizing “Vision-Centric Autonomous Driving Workshop” at CVPR2023. Submit your recent papers, and participate in our novel challenges!
|Feb 27, 2023||Four papers (few-shot neural rendering, representation learning for point clouds, end-to-end prediction for AV, and map learning) accepted to CVPR 2023!|
|Dec 30, 2022||Our workshop proposal “Data-Driven Visual Autonomous Driving (DDVAD)” has been accepted to CVPR2023. See you in Vancouver!|
|Sep 11, 2022||Two papers accepted to CORL 2022.|
|Sep 6, 2022||I joined Nvidia Research as a Research Scientist.|