Grant Boquet
About Grant Boquet
Grant Boquet is an Applied Machine Learning Group Leader at Lawrence Livermore National Laboratory, where he has worked since 2013. He holds a PhD in Mathematics from Virginia Tech and specializes in heterogeneous data analysis.
Work at Lawrence Livermore National Laboratory
Grant Boquet has been employed at Lawrence Livermore National Laboratory since 2013. He currently holds the position of Applied Machine Learning Group Leader, a role he has occupied since 2021. Prior to this, he served as a Senior Machine Learning Scientist for 11 years. His work at the laboratory focuses on applying machine learning techniques to various scientific challenges, particularly in the realm of heterogeneous data.
Education and Expertise
Grant Boquet earned his Bachelor of Science in Mathematics from Virginia Tech from 2002 to 2005. He continued his studies at the same institution, obtaining a Master of Science in Mathematics from 2005 to 2008. He further advanced his education by completing a Doctor of Philosophy in Mathematics at Virginia Tech from 2008 to 2010. His academic background equips him with a strong foundation in mathematical principles, which he applies in his research and professional roles.
Background
Before joining Lawrence Livermore National Laboratory, Grant Boquet worked at Virginia Tech as a Graduate Teaching Fellow from 2005 to 2007 and as a Research Assistant from 2004 to 2005. He also served as a Research Scientist at Metron, Inc. from 2007 to 2013. His experiences in academia and industry have contributed to his expertise in machine learning and data analysis.
Research Focus and Projects
Grant Boquet's research primarily centers on heterogeneous data, with a particular emphasis on text and discrete data types. He is the principal investigator for a team that develops models to connect text content with dynamics in social networks. Additionally, he is the lead developer of a parallel heterogeneous big data machine learning package known as pysparkplug, which facilitates advanced data processing and analysis.