About Me
Open-Source Model Serving
I am a core maintainer of vLLM-Omni and AFD Plugin and a member of the vLLM Project team. My current work focuses on efficient inference for omni-modality models and attention–FFN disaggregation.
Across these projects, I work on scalable serving systems, distributed inference, performance optimization, and open-source engineering that turns research models into practical deployments.
vLLM-Omni & AFD Plugin Core Maintainer
vLLM Project Team Member
MindScience & AI4Science
Scientific Machine Learning Research
AI4Science Research Background
My AI4Science work includes maintaining MindScience, Huawei's scientific computing platform with 6 specialized suites and 60+ AI models. This research applies machine learning to complex scientific problems.
My research spans computational fluid dynamics, spatiotemporal modeling, physics-informed neural networks, and physics-encoded machine learning, with publications across leading AI and scientific computing venues.
Core Technologies
Professional Experience
Research Scientist
Huawei 2012 Lab • Present
1. Core maintainer of vLLM-Omni and AFD Plugin, and a member of the vLLM Project team.
2. Develop high-performance infrastructure for multimodal model inference, distributed serving, and open-source AI systems.
3. Maintainer of MindSpore Science with a research background in AI4Science and scientific computing.
Ph.D. in Statistics and Operations Research
University of North Carolina at Chapel Hill • 2015-2020
Doctoral research in Statistics and Operations Research, focusing on optimization theory and computational methods. Developed expertise in convergence analysis for decomposition algorithms and large-scale optimization problems.
Bachelor's Degree in Mathematical Statistics
School for the Gifted Young, USTC • 2011-2015
Undergraduate education in Mathematical Statistics at the prestigious School for the Gifted Young at the University of Science and Technology of China, providing accelerated academic training and comprehensive foundation in mathematical theory, statistical methods, and quantitative analysis.
Featured Publications
Showcasing cutting-edge research in AI4Science and ML optimization
Conservation-informed Graph Learning for Spatiotemporal Dynamics
KDD 2025
SlotPi: Physics-informed Object-centric Reasoning Models
KDD 2025
PhyMPGN: Physics-encoded Message Passing Graph Network
ICLR 2025
Featured Projects
Leading initiatives in AI4Science and ML infrastructure
MindScience Platform
Lead maintainer of Huawei's flagship AI4Science platform with 6 specialized suites and 60+ AI models.
LLM Serving Research
Specialized research in large language model serving optimization with vLLM and vLLM-Ascend systems.
MindFlow - CFD Suite
Computational fluid dynamics solver with physics-informed AI and differentiable programming.