Dinghao YANG   杨丁豪

Senior System Engineer, NVIDIA

Training infrastructure for Physical AI

Hangzhou, China

Email: dinghaoy@nvidia.com;
             dinghowyang@gmail.com;
Google Scholar: Google Scholar Link
Github: https://github.com/Dinghow
Personal Blog: https://dinghow.site/dinghow-blog

Biography

I am a senior system engineer at NVIDIA, currently leading the infrastructure for the Diffusion RL stack within Cosmos-RL — a unified RL framework for foundation models spanning LLM, VLM, Diffusion, and VLA. I'm lucky to work with Jiaxin Cao, Junjie Bai and many other talented geeks. Before NVIDIA, I worked at Lepton AI (acquired by NVIDIA in 2025), where I contributed to the development of Lepton's LLM inference engine. Earlier, I was a core developer of BladeLLM — the inference engine of Alibaba Cloud's Machine Learning Platform for AI powering the Qwen series.

I received the M.S degree from the School of Electronic and Computer Engineering, Peking University, in 2023, and the B.E. degree from the School of Software Engineering, Tongji University, in 2020.

My interests include GenAI Infra, 3D Vision, Physical AI, and World Foundation Model.

Experience

Publications

Preprint Papers

World Simulation With Video Foundation Models for Physical AI
NVIDIA (I am a core contributor for the Diffusion RL training framework)
[PDF] [Code]
Cosmos-reason1: From physical common sense to embodied reasoning
NVIDIA (I am a core contributor for the SFT & RL training framework)
[PDF] [Code]

Published Papers

Unified Interactive Image Matting
Dinghao Yang, Bin Wang, Weijia Li, Yiqi Lin, Conghui He
ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), 2025
[PDF] [Code]
PointCHD: A Point Cloud Benchmark for Congenital Heart Disease Classification and Segmentation
Dinghao Yang, Wei Gao
IEEE Journal Of Biomedical & Health Informatics (JBHI), 2024
[PDF] [Code]
Exploring the User Guidance for More Accurate Building Segmentation from High-Resolution Remote Sensing Images
Dinghao Yang, Bin Wang, Conghui He, Weijia Li
International Journal of Applied Earth Observation and Geoinformation (JAG), 2024
[PDF] [Code]
Exploiting Manifold Feature Representation for Efficient Classification of 3D Point Clouds
Dinghao Yang, Wei Gao, Ge Li, Hui Yuan, Junhui Hou, Sam Kwong
ACM Transactions on Multimedia Computing, Communications and Applications (TOMM), 2023
[PDF] [Code]

Projects

Cosmos-RL
Cosmos-RL is a scalable Reinforcement Learning framework for foundation models, currently the only open-source RL framework that unifies LLM, VLM, Diffusion, and VLA training under a single abstraction. I lead the infrastructure for the WFM (Diffusion-based World Foundation Model) RL stack, covering training pipeline design and distributed system integration.
[Code]
Lepton Inference Engine
The LLM inference engine developed at Lepton AI (acquired by NVIDIA in 2025). I contributed to model implementation (LLM, low-latency TTS), performance optimization by CUDA kernels, and serving-side performance work.
[Lepton AI]
BladeLLM (Qwen Inference Engine)
BladeLLM is the production inference engine for the Qwen series at Alibaba Cloud. As a core developer, I owned model implementations, serving-side performance work, engine interface development, and Triton kernel optimizations.
[Introduction]
Intelligent Annotation for SenseBee
I am principally responsible for the interactive data annotation algorithms for SenseBee, the data platform for SenseTime AI research. We propose and deploy deep learning-based algorithms to accelerate manual data annotations (i.e. image segmentation, image matting, 3D object detection).
[Code]
OpenPointCloud
I participate in OpenPointCloud, an open-source algorithm library of deep learning-based point cloud compression & processing, mainly contrbute in the manifold learning-based point cloud representation learning method.
[Code]

Awards

Academic Activities