Eisha Nathan
About Eisha Nathan
Eisha Nathan is a Computational Scientist at Lawrence Livermore National Laboratory, where she has worked since 2018. She has a background in Mathematics and Computer Engineering from the University of Maryland and holds a Ph.D. in Engineering and Philosophy from Georgia Institute of Technology.
Work at Lawrence Livermore National Laboratory
Eisha Nathan has been employed as a Computational Scientist at Lawrence Livermore National Laboratory since 2018. In this role, she contributes to various computational projects, utilizing her expertise in mathematics and engineering. The laboratory is known for its research in national security, energy, and environmental sustainability, and Nathan's work supports these critical areas.
Previous Experience in Academia and Industry
Prior to her current position, Eisha Nathan held several roles in academia and industry. She worked at the University of Maryland as a Math Directed Reading Program participant in 2011 and served as a Gems100 Section Leader at the same institution. Nathan also gained experience as an Intern at the University of Maryland Baltimore County's Department of Computer Science in 2010. In the industry, she interned at Booz Allen Hamilton in various capacities, including as a Research Assistant in 2012 and as a Solutions Engineering Design Intern in 2013.
Graduate Research at Georgia Institute of Technology
Eisha Nathan was a Graduate Research Assistant at Georgia Institute of Technology from 2014 to 2018. During her time there, she engaged in research that combined her interests in engineering and philosophy. This experience contributed to her development as a computational scientist and prepared her for her current role at Lawrence Livermore National Laboratory.
Education and Expertise
Eisha Nathan earned her Bachelor of Science degree in Mathematics and Computer Engineering from the University of Maryland, completing her studies from 2010 to 2014. She furthered her education at Georgia Institute of Technology, where she obtained a Doctor of Philosophy degree in Engineering and Philosophy between 2014 and 2018. This educational background provides her with a strong foundation in both theoretical and applied aspects of computational science.