Profiles

Dr. Rabab Alomairy is an Ibn Rushd Assistant Professor of Computer Science at King Abdullah University of Science and Technology (KAUST). Her research is rooted in high-performance computing (HPC) and focuses on the co-design of algorithms, software, and hardware-aware systems for next-generation AI and scientific computing infrastructure. Her work spans task-based numerical libraries, GPU programming, scalable runtime systems, mixed-precision computing, efficient AI inference, and AI-accelerated scientific applications. Through her work, she develops scalable and efficient computing technologies that improve performance, memory efficiency, and system utilization on modern multicore, many-core, and accelerator-based architectures. Her broader goal is to enable practical, efficient, and scalable computing systems that accelerate scientific discovery and support national-scale AI innovation.

Dr. Alomairy has collaborated with leading research institutions and industry partners, including the Massachusetts Institute of Technology (MIT) through JuliaLab, NVIDIA, Oak Ridge National Laboratory (ORNL), the Innovative Computing Laboratory (ICL) at the University of Tennessee, and MINES ParisTech. During an internship at the University of Tennessee, she contributed to the U.S. Department of Energy-funded SLATE project, advancing next-generation numerical software for high-performance computing. She later joined MIT JuliaLab, where she worked on high-performance computing, task-based runtimes, GPU-accelerated numerical libraries, and AI-driven scientific computing applications. Through these collaborations, she has contributed to the development of scalable software and algorithms for next-generation AI and scientific computing systems.

In recognition of her research contributions, Dr. Alomairy was named a Rising Star in Computational and Data Sciences by the U.S. Department of Energy in 2022. She also led the first Julia tutorial for productive HPC at the Supercomputing Conference. Her work has scaled across some of the world's most powerful supercomputers and has received international recognition, including finalist honors for the ACM Gordon Bell Prize (2020), the Gauss Award and the IEEE Computer Society Technical Community on High Performance Computing (TCHPC) Early Career Researchers Award for Excellence in High Performance Computing (2025).

Before joining the KAUST faculty, Dr. Alomairy was a postdoctoral fellow at MIT’s JuliaLab and a recipient of the KAUST Ibn Rushd Fellowship. She also served as Senior AI Expert at Tahakom Saudi company, where she led initiatives in AI infrastructure, distributed AI systems, and sovereign AI technologies, helping bridge cutting-edge research with large-scale industrial deployment. A KAUST alumna, she earned both her M.S. and Ph.D. in Computer Science under the supervision of Professor David E. Keyes and Senior Research Scientist Hatem Ltaief.

Dr. Alomairy continues to advance sustainable high-performance computing while fostering interdisciplinary collaboration at the intersection of HPC and artificial intelligence, translating cutting-edge computational methods into solutions for real-world scientific challenges.

• High-performance computing for AI and scientific applications
• Co-design of algorithms, software, and hardware-aware systems
• Task-based numerical libraries and applications
• Dense linear algebra and scalable numerical algorithms
• GPU programming and accelerator-based computing
• AI infrastructure and large-scale AI systems
• Mixed-precision computing and numerical performance

Sahar Ammar is a Ph.D. candidate in Electrical and Computer Engineering at King Abdullah University of Science and Technology (KAUST). She holds an M.S. In Electrical and Computer Engineering from KAUST and a Diplôme d'ingénieur from École Polytechnique de Tunisie, and was a visiting researcher at Universitat Pompeu Fabra (UPF). She has contributed to several projects on intelligent and energy-efficient communication systems across maritime, aerial, and optical wireless networks.

Sahar research studies involve developing RL-based solutions for network optimization, mobility management, and resource orchestration in 5G/6G systems. Additionally, she has refined her expertise in optical wireless communication, gained through earlier work on Sun-Fi, a novel green solution for indoor environments based on sunlight modulation. She is also interested in interdisciplinary collaborations and excited by opportunities to tackle real-world challenges and advance global connectivity.

Sara Rojas Martinez is a Ph.D. student in the KAUST Image and Video Understanding Lab (IVUL) under the supervision of Professor Bernard Ghanem. Before joining KAUST, Sara obtained a master’s degree in Biomedical Engineering from Universidad de Los Andes, Bogotá, Colombia. 

Sara completed a research internship at Naver Labs Europe, where she worked on extending MAST3R to better understand humans in-the-wild. Her advisors were Gregory Rogez, Matthieu Armando, and Vincent Leroy.

Prior to that, Sara interned at Adobe Research, where she worked under the guidance of Kalyan Sunkavalli. She also collaborated with Reality Labs at Meta in Zurich, mentored by Albert Pumarola and Ali Thabet. Earlier, she conducted research at the University of Southern California with Autumn Kulaga.

Sara is interested in topics related to the investigation in Artificial Intelligence, 3D computer vision, deep learning, generative AI, and image processing. She has also worked on 3D reconstruction.

Sebastian Celis Sierra received the B.Eng. degrees in Electrical and Electronics Engineering from Universidad de los Andes, Bogotá, in 2018, and the M.S. and Ph.D. degrees in Electrical and Computer Engineering from King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia, in 2020 and 2026, respectively. He is currently a Postdoctoral Fellow at KAUST under the supervision of Hakan Bagci. His research interests include computational electromagnetics, specifically time-domain integral equation solvers, bianisotropic structures, and the characterization of 2D materials like graphene.

Sebastian's research focuses on formulating and implementing surface and volume integral-equation methods in both the time and frequency domains to capture complex electromagnetic behavior in arbitrarily shaped metasurfaces. By coupling these formulations with generalized sheet transition conditions (GSTCs), he aims to achieve both numerical efficiency and physical fidelity. His work advances the theoretical foundations and practical computational tools necessary for the next generation of communication systems.

Sihan Chen joined the PhD program in Statistics at King Abdullah University of Science and Technology (KAUST) in 2021, under the supervision of Prof. Marc G. Genton. Prior to that, he received his Bachelor’s and Master’s degrees from The Chinese University of Hong Kong. Sihan's doctoral research focused on developing robust and scalable statistical methods for spatial data analysis. His research interests include spatial statistics, robust estimation, high-performance computing, and applied data science.