Tim Moon
About Tim Moon
Tim Moon is a Data Scientist at Lawrence Livermore National Laboratory, where he has worked since 2017. He holds a Master's Degree in Computational and Mathematical Engineering from Stanford University and has experience in developing deep learning tools for high-performance computing environments.
Work at Lawrence Livermore National Laboratory
Tim Moon has been employed at Lawrence Livermore National Laboratory as a Data Scientist since 2017. His role involves leveraging data science techniques to support various research initiatives. He has contributed to the development of deep learning tools tailored for high-performance computing (HPC) environments. Prior to his current position, he completed a three-month internship at the laboratory in 2016, where he gained practical experience in a research setting.
Education and Expertise
Tim Moon holds a Master’s Degree in Computational and Mathematical Engineering from Stanford University, where he studied from 2014 to 2016. He also earned a Bachelor of Science in Physics from Rice University, completing his studies there from 2010 to 2014. His educational background provides a strong foundation in both theoretical and applied aspects of engineering and science, particularly in the context of data science and high-performance computing.
Previous Experience at Rice University
Before pursuing his advanced studies, Tim Moon worked as an Undergraduate Research Assistant at Rice University from 2009 to 2010. During this year-long position in Houston, Texas, he gained valuable research experience that contributed to his understanding of scientific inquiry and data analysis.
Internship Experience at NVIDIA
In 2015, Tim Moon served as a CUDA Software Intern at NVIDIA for three months in Santa Clara, California. This internship allowed him to gain hands-on experience in software development, specifically in the context of CUDA programming, which is essential for parallel computing and deep learning applications.
Development of LBANN Toolkit
Tim Moon has been involved in the development of LBANN, a deep learning toolkit specifically designed for high-performance computing (HPC) architectures. This initiative reflects his expertise in creating tools that enhance the capabilities of HPC systems, particularly in the realm of deep learning, since he has been active in this area since 2017.