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Hi, I'm Jianhua Han (韩建华)

Research Scientist · Building the Future of Physical AI

"Intelligence can be defined as self-motivated skill-acquisition efficiency."

🎓 Google Scholar 🐙 GitHub ✉️ hanjianhua2012@gmail.com

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🚀 About Me

Currently a Research Scientist at 2030 Lab, Yinwang Intelligent Technology Co. Ltd. Previously at Huawei Noah's Ark Lab.

My passion lies in Vision-Language Models (VLM), VLA Models, World Models, and Physical AI — bridging Autonomous Driving & Robotics with intelligence.

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Location

Shanghai, China

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Education

M.S. & B.S. @ SJTU

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Focus

VLM · VLA · Physical AI

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📢 Join Us!

We are actively looking for self-motivated Interns and Full-time Researchers/Engineers at 2030 Lab, Yinwang.

Focus Areas: VLM, Physical AI, VLA, World Models, End-to-End Autonomous Driving, and General-purpose Agents.
Interested in building the next generation of intelligent systems? Reach out at hanjianhua2012@gmail.com 🎉

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🛠 Experience

Research Scientist @ 2030 Lab, Yinwang (2025 – Present)

Constructing research on VLM, VLA models and World Models for Physical AI.
Focusing on visual understanding, reasoning, and decision-making in complex open-world environments (AD & Robotics).

Research Scientist @ Huawei Noah's Ark Lab (2019 – 2025)

Foundation Models: Led the development of Large-scale Multi-modal Foundation Models (30B+ parameters).
Model architecture design, pre-training strategies, and data optimization — achieving superior results on MME, DocVQA, and MathVista.
Open-Vocabulary Perception: Developed the DetCLIP series, achieving No.1 on COCO, Waymo 2D, and LVIS leaderboards.

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📝 Selected Publications

Vision-Language-Action & Physical AI

Percept-WAM: Perception-Enhanced World-Awareness-Action Model for Robust End-to-End Autonomous Driving. NEW
Reason2drive: Towards interpretable and chain-based reasoning for autonomous driving. ECCV
M Nie, R Peng, C Wang, X Cai, J Han, H Xu, L Zhang
Navcot: Boosting LLM-based vision-and-language navigation via learning disentangled reasoning. TPAMI
B Lin, Y Nie, Z Wei, J Chen, S Ma, J Han, H Xu, X Chang, X Liang
SeePhys: Does Seeing Help Thinking? – Benchmarking Vision-Based Physics Reasoning. NeurIPS
HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving. IJCV
X Ding, J Han, H Xu, W Zhang, X Li

Multimodal Foundation Models

ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance. ICCV
C Wang, G Lu, J Yang, R Huang, J Han, L Hou, W Zhang, H Xu
ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement.
SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook.
Emova: Empowering language models to see, hear and speak with vivid emotions. CVPR
Gaining Wisdom from Setbacks: Aligning Large Language Models via Mistake Analysis. ICLR

Open-Vocabulary Perception (DetCLIP Series)

DetCLIP: Dictionary-enriched visual-concept paralleled pre-training for open-world detection. NeurIPS
L Yao*, J Han*, Y Wen, et al.
DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment. CVPR
DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection. CVPR
DetGPT: Detect what you need via reasoning. EMNLP