Ben Huntington
About Ben Huntington
Ben Huntington is an AI/Simulation Developer at Emerson Automation Solutions in Houston, Texas, with a background in data science and computational physics.
Current Role at Emerson Automation Solutions
Ben Huntington currently serves as an AI/Simulation Developer at Emerson Automation Solutions in Houston, Texas. In this role, he utilizes his extensive background in computational fluid mechanics and data science to develop advanced simulation tools. His work includes integrating AI algorithms into existing systems to enhance predictive capabilities and streamline operational processes.
Previous Experience at AIM2
Ben Huntington worked as a Data Scientist at AIM2 from 2019 to 2020 for a period of 10 months in the Austin, Texas Area. During this time, he was involved in developing data-driven solutions to optimize business processes. His role included the analysis and interpretation of complex datasets to provide actionable insights for decision-making.
Education and Computational Fluid Mechanics Expertise
Ben Huntington holds a Doctor of Philosophy (PhD) in Computational Fluid Mechanics from The University of Texas at Austin, earned over six years from 2011 to 2017. His doctoral studies focused on advancing theoretical and computational methods in fluid mechanics. Additionally, he earned a Bachelor of Science (BS) in Chemical Engineering from Oregon State University, which he completed in 2011. His educational background equips him with a robust foundation in chemical engineering and computational techniques.
Programming and Technical Skills
Ben Huntington is proficient in multiple programming languages, including C, C++, Fortran, Python, and MATLAB. His technical skill set also encompasses parallel computing techniques using openmp and MPI. These skills are applied in his current and past projects, allowing him to implement efficient computational solutions for complex engineering and data science problems.
Machine Learning and Data Science Projects
Ben Huntington has developed a range of machine learning and data science projects, which are available on GitHub. One notable project includes a machine learning-based solution for leak detection in shut-in pipelines, where he implemented a signal analysis solution to accurately determine leak locations. His proactive contributions in this field demonstrate his capability to apply theoretical knowledge to practical, real-world problems.