Waymo, Staff Research Scientist 2026–Present
Pretraining, target-domain mid-training, and RL post-training for Waymo Gemini. Developing diffusion-model post-training methods and RL training infrastructure, with autonomous driving as the primary application.

• Reward modeling. Led critic-based reward design under teacher-student information asymmetry. Grounded rewards in student-observable evidence to avoid reinforcing unsupported reasoning even when final outcomes were correct. Developed format and process rewards.
• Accuracy-efficiency co-design. Co-optimized pretraining architectures and kernels for task accuracy and end-to-end inference latency on target devices, developing and evaluating Gated DeltaNet and MuxNet variants, state experts, and multi-exit designs. Guided architectural decisions through empirical evaluation of model quality, training stability, and on-device efficiency.

NVIDIA Research, Senior Research Scientist 2023–2026
Core member of Nemotron-Diffusion-VL, responsible for data recipes, evaluation, VLM implementation, and training infrastructure. Led research on multimodal perception-to-action agents, including language-grounded scene understanding, trajectory generation, teacher-student distillation, closed-loop evaluation, and reinforcement learning, applied to end-to-end autonomous driving through the HydraMDP family and OmniDrive.

NVIDIA Research, Research Scientist 2022–2023
Core team member of NV-CLIP; led pre-training and resolved loss-divergence issues. Developed MMAL, the CVPR 2023 mask auto-labeling framework.

NVIDIA Research, Research Intern 2020
Researched weakly supervised instance segmentation on MS COCO.

ByteDance AI Lab, Research Intern 2018
Contributed to a recommendation-system warmup project that reduced daily warmup cost by five million CNY.

• Nemotron-Diffusion-VL — Core member of NVIDIA's first diffusion VLM.
• End-to-End Driving at Scale — 1st place at CVPR 2025 and CVPR 2024 (Team Lead).
• 3D Occupancy Challenge — 1st place at CVPR 2023.
• Robust Vision Challenge — 1st place in the 2022 semantic segmentation track.
• MS COCO 2017 — 1st place in object detection and 2nd place in instance segmentation.

For a complete list, please see my Google Scholar profile.

The first-place weakly supervised instance segmentation model using box labels.

The multi-representation, multi-scale, mutual-relation 3D object detector with transformers.

3D Object Detection with the effective dynamic attention module.

A real-time and high-performance object detector.

Modeling point cloud by leveraging geometric information of point clouds.

Generating instance segmentation candidates by leveraging multi-scale feature pyramids.

• ICPC 2015 Shenyang Regional, Silver Medal (Rank 18/≈300)
• National Olympiad in Informatics of China 2013, Bronze (Rank 122/≈400)

• Temporal-based perception for autonomous systems and applications, US Patent 12,625,926 (2026) (Choi, Lopez, Lan, Asgarieh, Yu)
• Occupancy prediction using forward-backward view transformation, US Patent 12,515,706 (2026) (Li, Yu, Austin, Lan, Kautz, Lopez)
• Point-level supervision for video instance segmentation, US Patent App. 18/395,198 (Yu, Huang, Huang, Lan, Radhakrishnan, Alvarez)
• Class agnostic object mask generation, US Patent 12,614,284 (2026) (Lan, Yu, Radhakrishnan, Lopez, Anandkumar)
• Object detection, instance segmentation, and semantic correspondence from bounding box supervision, US Patent App. 17/177,068 (Yu, Lan, Choy, Radhakrishnan, Liu, Zhu, Anandkumar)