I am a principal research scientist and research lead at the Learning and Perception Research Group, NVIDIA Research. Before joining NVIDIA in 2018, I obtained Ph.D. in ECE from Carnegie Mellon University in 2017, and M.Phil. in ECE from The Hong Kong University of Science and Technology in 2012. I graduated with a bachelor's degree from the Union Class of Electrical Engineering (FENG Bingquan Pilot Class), South China University of Technology in 2008.

I am interested in building general autonomy and intelligence across both virtual and physical domains. My recent focus lies in Vision Transformers, LLMs, multimodal LLMs, and vision-language-action (VLA) models, with applications spanning open-world understanding, reasoning, AV/robot perception-planning, and agentic systems. I have led or contributed to numerous flagship research efforts and products at NVIDIA, including SegFormer (Most Influential NeurIPS Paper, Demo), VoxFormer, FB-BEV/FB-OCC, (CVPR23 3D Occ Pred Challenge winner, video), Hydra-MDP (CVPR24 E2E Driving Challenge winner, video), the Eagle VLM project, Nemotron, Llama-Nemotron-VL, and GR00T N1/GR00T N1.5 (NVIDIA’s foundation models for humanoid robots). I also participated in designing NVIDIA’s next-generation end-to-end autonomous driving system. My works are characterized by state-of-the-art performance, scalable architectures, and data-centric strategies towards real-world generalization.

Please refer to Google Scholar for the list of my latest publications.

• Winner, CVPR24 Challenge on End-to-End Driving at Scale

• 2nd Place, CVPR24 Challenge on Driving with Language

• Winner, CVPR23 Challenge on 3D Occupancy Prediction

• Winner, ECCV22 Robust Vision Challenge (RVC) on Semantic Segmentation

• Winner, CVPR18 Autonomous Driving Challenge (WAD) on Domain Adaptation

• 2nd Place, ICMI15 EmotiW Challenge on Static Facial Expression Recognition

• Best Paper Award, BMVC 2020

• Best Paper Award, WACV 2015

• Best Student Paper Award, ISCSLP 2014