James Diffenderfer
About James Diffenderfer
James Diffenderfer is a Computational Scientist currently working at Lawrence Livermore National Laboratory, specializing in model compression and robustness in machine learning. He has a diverse academic background with degrees in Mathematics and Computer Science from Georgia Southern University and the University of Florida.
Work at Lawrence Livermore National Laboratory
James Diffenderfer has been employed as a Computational Scientist at Lawrence Livermore National Laboratory since 2017. His work focuses on advancing computational methods and applications within the laboratory's research initiatives. He has contributed to various projects that enhance the understanding and implementation of computational science, particularly in the areas of model compression and robustness in machine learning.
Education and Expertise
James Diffenderfer holds a Doctor of Philosophy (PhD) in Applied Mathematics with a specialization in Optimization from the University of Florida, which he completed from 2015 to 2020. He also earned a Master of Science (MS) in Computer Science from the same institution between 2017 and 2019. Prior to that, he obtained a Bachelor of Science (BS) in Mathematics from Georgia Southern University in 2011 and a Master of Science (MS) in Mathematics from the same university in 2013. His educational background provides a strong foundation for his expertise in computational science.
Background
James Diffenderfer began his academic journey at Georgia Southern University, where he studied Mathematics and completed both his undergraduate and master's degrees. He worked as a Student Employee in the Office of Admissions from 2008 to 2011. Afterward, he served as a Visiting Instructor at Georgia Southern University from 2011 to 2015. He then transitioned to the University of Florida, where he worked as a Graduate Research Assistant from 2015 to 2020, gaining valuable research experience before joining Lawrence Livermore National Laboratory.
Research Contributions
Since joining Lawrence Livermore National Laboratory, James Diffenderfer has engaged in research related to approximate computing. His work emphasizes the importance of model compression and robustness in machine learning, contributing to the advancement of computational techniques in this area. His research efforts aim to improve the efficiency and reliability of computational models, which are critical in various scientific applications.