Daniel Fenn, PhD

Daniel Fenn, PhD

Computational Scientist @ Lawrence Livermore National Laboratory

About Daniel Fenn, PhD

Daniel Fenn, PhD, is a computational scientist with a focus on developing models and algorithms for analyzing physical phenomena. He has over 10 years of experience in high-performance computing and has worked at various prestigious institutions including NASA and the United States Air Force.

Work at Lawrence Livermore National Laboratory

Daniel Fenn has been employed as a Computational Scientist at Lawrence Livermore National Laboratory since 2019. In this role, he focuses on developing models and algorithms that enhance the understanding and prediction of physical phenomena. His work involves utilizing high-performance computing to analyze large volumes of unorganized data, contributing to advancements in various scientific fields.

Education and Expertise

Daniel Fenn holds a Doctor of Philosophy (PhD) in nuclear science and engineering from the Massachusetts Institute of Technology. He completed his PhD from 2011 to 2016. Prior to this, he earned a Bachelor of Science (BS) in Physics from Utah State University from 2007 to 2011. His educational background equips him with a strong foundation in computational science and physics, enabling him to tackle complex engineering and operational questions.

Background

Daniel Fenn has a diverse professional background in computational science and data analysis. He served as a Research Fellow at Los Alamos National Laboratory for three months in 2013 and worked as a Research Assistant at NASA Goddard Space Flight Center for three months in 2010. He also held positions as a Data Scientist for the United States Air Force from 2017 to 2019 and as an Undergraduate Research Assistant at Space Dynamics Laboratory from 2008 to 2011.

Achievements

Daniel Fenn is recognized as one of the first graduates of the NIF–MIT Ph.D. Thesis Program. He has accumulated ten years of experience in developing theories and applying solutions across various domains, including physics and engineering. His expertise in high-performance computing and data exploitation has positioned him as a valuable contributor to scientific research and development.

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