Nitesh Attal, Ph.D.
About Nitesh Attal, Ph.D.
Nitesh Attal, Ph.D., is a Principal Engineer with extensive experience in statistical analysis and high-performance computing systems. He has worked in various engineering roles, including research positions at Convergent Science and the University of North Carolina at Charlotte.
Work at Convergent Science
Nitesh Attal has been a Principal Engineer at Convergent Science since 2022, contributing to advancements in high-performance computing systems. Prior to this role, he served as a Senior Research Engineer from 2018 to 2022 and as a Research Engineer from 2016 to 2018. His work focuses on modeling multi-physics systems and utilizing machine learning for combustion predictions. Attal's expertise includes high-fidelity modeling techniques such as Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS), which are essential for turbulent combustion analysis.
Education and Expertise
Nitesh Attal holds a Doctor of Philosophy (Ph.D.) in Mechanical Engineering from the University of North Carolina at Charlotte, where he studied from 2012 to 2016. He also earned a Master's degree in Mechanical Engineering from the same institution from 2010 to 2012. His undergraduate degree, a Bachelor of Engineering (B.E.) in Mechanical, was obtained from Doctor Babasaheb Ambedkar Marathwada University between 2002 and 2006. Attal's educational background supports his expertise in statistical analysis of large datasets and machine learning applications in combustion optimization.
Background
Before joining Convergent Science, Nitesh Attal worked as a Research Assistant at the University of North Carolina at Charlotte from 2010 to 2016. His early career included a position at Force Motors in India as a Graduate Engineer Trainee in the R&D Engine department for nine months in 2007. This diverse background has equipped him with a solid foundation in mechanical engineering and research methodologies.
Achievements
Throughout his career, Nitesh Attal has contributed to various projects related to thermal management of electric motors and reacting multiphase flows. His work on modeling systems such as LiDAR cleaning sprays and battery thermal runaway showcases his ability to integrate complex engineering concepts. Attal's proficiency in using genetic algorithms for optimization further highlights his innovative approach to engineering challenges.