Ali Navid
About Ali Navid
Ali Navid is a Biomedical Scientist with a Ph.D. in Biophysical Chemistry from Indiana University Bloomington. He has extensive experience in molecular biology and reaction kinetics, currently working as a Computational Scientist at ATOM in the San Francisco Bay Area since 2017.
Work at Lawrence Livermore National Laboratory
Ali Navid has been employed at Lawrence Livermore National Laboratory as a Biomedical Scientist since 2009. His role involves applying his expertise in molecular biology and reaction kinetics to advance research in life sciences. Prior to this position, he worked as a Postdoctoral Research Scientist at the same laboratory from 2006 to 2009, contributing to various projects that enhanced the understanding of biochemical processes.
Current Role at ATOM
Since 2017, Ali Navid has served as a Computational Scientist at ATOM | Accelerating Therapeutics for Opportunities in Medicine, located in the San Francisco Bay Area. In this capacity, he utilizes his skills in molecular biology and computational modeling to support drug metabolism research and development. His work focuses on leveraging computational techniques to improve therapeutic opportunities.
Education and Expertise
Ali Navid holds a Ph.D. in Biophysical Chemistry from Indiana University Bloomington, where he studied from 1998 to 2004. He also earned a B.S. in Chemistry from the University of Maryland (1988-1992) and an M.S. in Physical Chemistry from the University of North Dakota (1995-1998). His educational background provides a strong foundation in molecular biology and reaction kinetics, which are essential for his research in life sciences.
Previous Research Experience
Before his current roles, Ali Navid worked at Indiana University Bloomington as a Graduate Student and Honorary Researcher/Faculty from 1998 to 2015. He also completed a Postdoctoral Fellowship at The Smith Kettlewell Eye Research Institute from 2004 to 2006. His extensive research experience includes contributions to drug metabolism through computational modeling techniques, enhancing the understanding of biochemical processes.