Jin Yao
About Jin Yao
Jin Yao is a computational physicist and applied mathematician with extensive experience in developing advanced computational tools. He has worked at Lawrence Livermore National Laboratory since 2002 and holds degrees from several prestigious institutions, including a PhD in Applied Mathematics from the University of Illinois at Urbana-Champaign.
Work at Lawrence Livermore National Laboratory
Jin Yao has been employed at Lawrence Livermore National Laboratory since 2002, serving as a Computational Physicist and Applied Mathematician. Over the years, Yao has contributed significantly to various projects and tools within the laboratory. Notably, Yao developed the particle-based surface dynamical operation tool package SDOT, which advanced surface dynamics operations. Additionally, Yao created a curved interface reconstruction package for ALE3D, enhancing its capabilities in managing complex geometries. Yao's work includes developing a detonation shock front tracking package for ALE3D and implementing mesh relaxation methods, which improved simulation efficiency.
Education and Expertise
Jin Yao holds a Doctor of Philosophy (PhD) in Applied Mathematics from the University of Illinois at Urbana-Champaign, where studies were completed from 1990 to 1996. Prior to this, Yao earned a Master of Science (M.S.) in Continuum Mechanics from the same institution, studying from 1990 to 1995. Yao also holds a Master of Science (M.S.) in Computational Physics from the Institute of Applied Mathematics at the Chinese Academy of Sciences, completed from 1983 to 1989. Yao began academic pursuits with a Bachelor of Science (B.S.) in Theoretical and Mathematical Physics from the University of Science and Technology of China, achieved from 1978 to 1983.
Background
Jin Yao's academic and professional journey spans several prestigious institutions and roles. Yao's foundational studies in Theoretical and Mathematical Physics laid the groundwork for a career focused on computational methods in physics and mathematics. After completing undergraduate studies, Yao advanced through various graduate programs, culminating in a PhD. Yao's extensive experience at Lawrence Livermore National Laboratory reflects a commitment to applying mathematical principles to solve complex physical problems.
Achievements
Throughout a career spanning over two decades, Jin Yao has developed several significant computational tools and methods. Yao's contributions include fast-convergent mesh smoothing algorithms and general computational geometry methods for INGRID, which enhanced the tool's performance. Additionally, Yao designed a fast non-negative least-squared solver for large-scale optimization with RNAK, addressing challenges in computational optimization. Yao's work on conservative surface relaxation methods for MESQUITE further showcases expertise in computational geometry.