Xiao Chen
About Xiao Chen
Xiao Chen is a Computational Scientist at Lawrence Livermore National Laboratory, where he has worked since 2013. He holds a Ph.D. in Computational and Applied Mathematics and has developed innovative frameworks for climate prediction and uncertainty quantification.
Work at Lawrence Livermore National Laboratory
Xiao Chen has been employed at Lawrence Livermore National Laboratory as a Computational Scientist since 2013. In this role, he has contributed to various projects and initiatives focused on computational modeling and data analysis. Prior to his current position, he served as a Postdoctoral Researcher Staff at the same laboratory from 2011 to 2013. His work encompasses developing frameworks and tools for climate prediction and statistical inference, utilizing advanced computational techniques.
Education and Expertise
Xiao Chen holds a Doctor of Philosophy (Ph.D.) in Computational and Applied Mathematics from Florida State University, where he studied from 2006 to 2011. He also earned a Master's degree in Applied Mathematics from Zhejiang University, studying from 2004 to 2006. Additionally, he completed his Bachelor's degree in Applied Mathematics at Fuzhou University from 2000 to 2004. His educational background provides a strong foundation for his expertise in computational science and data-driven methodologies.
Research Contributions
Xiao Chen has proposed a scalable and efficient quantitative framework for climate prediction, which has a pending budget of $15 million and involves collaboration among 20 participants from four Department of Energy (DOE) labs and three universities. He has also developed a fast random field inversion technique that integrates kernel learning with stochastic data assimilation. Furthermore, he introduced a machine learning-based uncertainty quantification framework aimed at assessing data-driven uncertainty.
Projects and Initiatives
Xiao Chen leads a $2 million project focused on developing a high-performance computing (HPC)-enabled statistical inference method that utilizes kernel-manifold techniques. He has also played a significant role in porting the DOE Atmospheric Radiation Measurement (ARM) data Best Estimate (ARMBE) data product into operational use, enhancing the accessibility and utility of critical climate data.