Nat Trask
About Nat Trask
Nat Trask is a Senior Member of the Technical Staff at Sandia National Laboratories, specializing in physics-compatible meshfree discretization tools for scientific machine learning applications.
Title
Nat Trask holds the position of Senior Member of the Technical Staff at Sandia National Laboratories. He has been serving in this capacity since 2018 in the Albuquerque, New Mexico area.
Professional Experience at Sandia National Laboratories
Nat Trask has significant experience at Sandia National Laboratories. He has worked as an NSF fellow from 2016 to 2018 and then advanced to his current role as Senior Member of the Technical Staff starting in 2018. His work has primarily been focused in the Albuquerque, New Mexico area, where he contributes to technical projects and research.
Academic Roles and Research
Nat Trask has an extensive academic background. He has served as a Visiting Professor at Brown University for two months in 2015. Additionally, he held the position of Lecturer at Kobe University/Brown University from 2013 to 2014 in Kobe, Japan. Earlier in his career, he worked as a Research Assistant at Brown University from 2010 to 2015 and at the University of Massachusetts from 2008 to 2010. He also gained experience as a Teaching Assistant at the University of Massachusetts from 2006 to 2008.
Educational Background
Nat Trask has earned advanced degrees in both applied mathematics and mechanical engineering. He obtained his Doctor of Philosophy (Ph.D.) in Applied Mathematics from Brown University, where he studied from 2010 to 2015. Prior to this, he completed a Master's degree in Mechanical Engineering with a focus on Fluid Mechanics from the University of Massachusetts Amherst, where he studied from 2008 to 2010. Additionally, he holds a Bachelor of Science (B.S.) degree in Applied Mathematics and Mechanical Engineering from the same institution, earned between 2004 and 2008.
Specialization and Research Focus
Nat Trask specializes in the development of physics-compatible meshfree discretization tools for scientific machine learning applications. His research interests are diverse, covering complex problems in combustion, material science, climate, and subsurface flow. He utilizes advanced mathematical and engineering techniques to address these challenging issues.