Tim Osborne
About Tim Osborne
Tim Osborne is a Data Platform Engineer at Oak Ridge National Laboratory, specializing in geophysical data processing and machine learning techniques. He holds a Bachelor of Science in Electrical and Computer Engineering from The University of Texas at Austin and a Bachelor of Science in Mathematics from Hardin-Simmons University.
Current Role at Oak Ridge National Laboratory
Tim Osborne serves as a Data Platform Engineer at Oak Ridge National Laboratory, a position he has held since 2021. In this role, he focuses on data platform development and engineering, contributing to advanced research initiatives. His expertise in data processing and machine learning plays a crucial role in the laboratory's projects.
Previous Experience at ConocoPhillips
Osborne worked at ConocoPhillips from 2011 to 2021, where he held two positions: HPC Systems Analyst and HPC Research Software Engineer. During his time as an analyst, he focused on high-performance computing systems, while in his role as a research software engineer, he specialized in optimizing geophysical algorithms. His work included developing a seismic processing system that utilized Spark as a backend.
Educational Background
Tim Osborne earned a Bachelor of Science in Mathematics from Hardin-Simmons University, where he studied from 2003 to 2007. He later attended The University of Texas at Austin, obtaining a Bachelor of Science in Electrical and Computer Engineering from 2009 to 2011. His educational background provides a solid foundation for his work in data engineering and geophysical data processing.
Teaching Experience at Georgetown ISD
Before entering the engineering field, Osborne worked as a Math Teacher at Georgetown Independent School District for 10 months in 2007 and 2008. This experience contributed to his understanding of mathematical concepts, which he later applied in his engineering roles.
Technical Expertise in Geophysical Data Processing
Tim Osborne has specialized in geophysical data processing, implementing techniques such as full waveform inversion and noise reduction using machine learning. He is proficient in optimizing various geophysical algorithms, including least squares migration, reverse time migration, Kirchhoff migration, and 3D surface related multiple elimination.