Samar Aseeri
- Computational Scientist, Computer Science
Dr. Samar Aseeri is a computational scientist specializing in high-performance computing (HPC), performance engineering, scalable scientific computing, and emerging quantum computing workflows. Her work focuses on performance analysis and optimization on modern supercomputing architectures, scalable numerical methods, and hybrid quantum–classical approaches for complex scientific applications.
Biography
Dr. Samar Aseeri has more than 15 years of experience in supercomputing and scientific computing. She received her Ph.D. in Applied Mathematics from Umm Al-Qura University, where she developed a strong analytical foundation in mathematical modeling and computational science. During her higher education, she studied quantum mechanics on two separate occasions, which contributed to her long-term interest in quantum computing and its computational foundations.
She later received advanced training in supercomputing at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, and subsequently supported the Shaheen user community at the KAUST Supercomputing Laboratory (KSL), focusing on scalable performance analysis and HPC optimization.
She has been actively engaged with the international scientific computing community, including early participation in major supercomputing conferences such as ISC High Performance and SC, where she followed emerging quantum computing tracks and sessions during the formative stages of the field.
At King Abdullah University of Science and Technology (KAUST), she continues to advance HPC research while contributing to hybrid quantum–classical computing workflows, including instructional support for KAUST’s early quantum algorithms course.
Research Interests
Dr. Aseeri's research interests span high-performance computing, applied computational mathematics, scalable scientific applications, and quantum computing technologies. Current focus areas include FFT algorithms, performance tooling, application profiling, parallel benchmarking, and hybrid quantum–classical methods for scientific and optimization problems.