Profiles

KAUST alumna Dr. Rabab Alomairy earned both her M.S. and Ph.D. in computer science from the King Abdullah University of Science and Technology (KAUST) under the supervision of Professor David E. Keyes and Senior Research Scientist Hatem Ltaief. She is currently a postdoctoral fellow at MIT’s JuliaLab and a recipient of the KAUST Ibn Rushd Fellowship.

Her research spans high-performance computing (HPC), task-based numerical libraries, GPU programming and AI-accelerated scientific applications, with emphasis on performance optimization for multicore and many-core architectures. Dr. Alomairy has collaborated with leading institutions, including Oak Ridge National Laboratory, the Innovative Computing Laboratory at the University of Tennessee and MINES ParisTech, contributing to the DOE-funded SLATE project during her internship at UTK.

In recognition of her impactful work, 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 the world’s top supercomputers and earned international honors, including a finalist recognition 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).

Dr. Alomairy continues to advance sustainable computation and foster collaboration across disciplines, translating advances in HPC and AI into real-world impact.

• Task-based numerical libraries and applications
• Performance optimizations
• Artificial intelligence at large scale
• Dense linear algebra

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.

Sakhaa Alsaedi is a Ph.D. graduate in Computer Science at King Abdullah University of Science and Technology (KAUST), under the supervision of Prof. Xin Gao and Prof. Takashi Gojobori. She received her bachelor’s degree in Computer Science from Taibah University in 2018 and her master’s degree in Computer Science from KAUST in 2020. She is the founder of the Medvation startup company, inventing educational kits that teach children concepts of robotics and Machine Learning (ML) in fun ways. She worked as a product developer at the Namma Al-Munawara company, Madinah.

Sakhaa's research focuses on developing principled computational frameworks for causal reasoning in medical digital twin systems by integrating multimodal biomedical data. More broadly, her work includes multi-omics data integration, causal biomedical knowledge graph construction, causal reasoning framework development, and genetic risk factor analysis. She aims to develop computational approaches that advance the understanding of complex biological systems and genetic risk factors, ultimately supporting precision medicine and broader data-driven discoveries in biomedicine.

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.