David Quint
About David Quint
David Quint is a computational physicist and engineer currently employed at Lawrence Livermore National Laboratory since 2020. He holds a PhD in Condensed Matter and Materials Physics from Syracuse University and has extensive experience in image data analysis and project management.
Work at Lawrence Livermore National Laboratory
David Quint has been employed at Lawrence Livermore National Laboratory as a Computational Physicist/Engineer since 2020. In this role, he focuses on computational projects, leveraging his expertise in image data analysis and optimization. His work contributes to the laboratory's mission of advancing scientific knowledge and technological innovation.
Education and Expertise
David Quint holds a Bachelor of Science (B.S.) in Physics from the University of California, Santa Cruz, which he completed from 2000 to 2003. He furthered his education at Syracuse University, where he earned a Doctor of Philosophy (PhD) in Condensed Matter and Materials Physics between 2005 and 2011. His educational background is complemented by a High School Diploma in Automotive Engineering Technology from Don Bosco Technical Institute.
Background in Research and Academia
David Quint has a diverse academic background, having worked as a Graduate Researcher at Syracuse University from 2005 to 2011. He also served as a Postdoctoral Researcher at UC Merced from 2011 to 2014, and at Stanford University and Carnegie Institution for Science from 2014 to 2016. His experience spans multiple disciplines and includes managing complex projects and collaborating in multidisciplinary groups.
Publications and Presentations
David Quint has authored and co-authored several research manuscripts published in competitive peer-reviewed journals. He has actively presented his work at numerous national conferences, showcasing his contributions to the field of computational physics and engineering.
Technical Skills and Project Management
David Quint possesses extensive experience in high throughput image analysis using Matlab. He has demonstrated the ability to manage and lead multiple complex projects simultaneously, with a focus on optimizing computational processes. His approach to problem-solving involves continuous learning and refining solutions to achieve efficiency.