Saad Khairallah
About Saad Khairallah
Saad Khairallah is a Senior Computational Engineer and Physicist at Lawrence Livermore National Laboratory, recognized for his contributions to computational modeling and deep learning applications in advanced manufacturing. He has developed significant tools and methods, including a Python-based test suite and the ALE3D digital twin predictive model, and has published extensively in high-impact journals.
Work at Lawrence Livermore National Laboratory
Saad Khairallah has worked at Lawrence Livermore National Laboratory (LLNL) since 2008, currently holding the position of Senior Computational Engineer/Physicist. His role involves leading various thrust groups and developing high-fidelity models on supercomputers, contributing to multi-million dollar projects for over a decade. He has been instrumental in advancing computational engineering and physics through the development of a Python-based test suite that automates the verification of complex models, significantly reducing analysis and debugging time.
Education and Expertise
Saad Khairallah earned a PhD in computational physics from the University of Illinois Urbana-Champaign, where he studied from 2000 to 2007. His academic background laid the foundation for his expertise in computational methods and advanced simulations. He also served as a Graduate Research Assistant at the same institution from 2002 to 2007. Following his doctoral studies, he worked as a postdoctoral researcher at the University of California, Berkeley for one year, and at LLNL for three years, further enhancing his skills in computational engineering.
Achievements
Throughout his career, Saad Khairallah has made significant contributions to the fields of computational engineering and physics. He co-developed quantum Monte Carlo methods to analyze plasmas under solar conditions, benefiting the National Ignition Facility at LLNL. His work includes the creation of the ALE3D digital twin predictive model for additive manufacturing, which aids engineers in producing complex parts with enhanced mechanical properties. He has filed several patents and published numerous articles in high-impact journals, with some of his work being among the most cited in the literature.
Research and Development Initiatives
Saad Khairallah is currently focused on accelerating models using deep learning neural networks, which has led to the development of a state-of-the-art digital twin for advanced manufacturing processes. His extensive validation against analytical test problems has established a physics-based criterion that improves energy conservation in high-fidelity simulations. His research initiatives reflect a commitment to integrating cutting-edge technology with practical applications in manufacturing and engineering.