Nadine Chang
Hi! I am a Senior Research Scientist at NVIDIA, part of the Spatial Intelligence Lab. I currently work on multimodal large language models (MLLMs) — for data selection and generation, the pursuit of hallucination-free and robust MLLMs, and perception tasks more broadly.
I received my Ph.D. from Carnegie Mellon University, where I was advised by Martial Hebert and Michael Tarr. My thesis focused on computer vision and language models that operate under real-world data distributions and applications. Prior to my Ph.D., I completed my Masters at CMU, where I was advised by Abhinav Gupta.
I was honored to be selected as an NSF GRFP fellow in 2018.
news
| Jun 24, 2026 | ZTRS, our zero-imitation end-to-end autonomous driving approach with trajectory scoring, was accepted to ECCV 2026. |
|---|---|
| May 05, 2026 | Our ICML 2026 position paper, Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development, was accepted. |
| Feb 27, 2026 | Two papers accepted to the CVPR 2026 main conference — on multimodal hallucination mitigation and scaling-aware data selection for autonomous driving. A third paper, on scalable prompting for AV video captioning, was accepted to a CVPR 2026 workshop. |
| Jan 20, 2026 | DriveCritic, on context-aware, human-aligned evaluation for autonomous driving with vision-language models, was accepted to ICRA 2026. |
| Oct 28, 2025 | GHOST, our multi-round consistency benchmark for hallucinations, was accepted to WACV 2026. |