Profiles

Ibrahim Abdelaziz is a Senior Research Scientist at IBM Research, where he focuses on enhancing the capabilities of Large Language Models (LLMs), particularly on improving their agentic and reasoning abilities. Prior to this, Ibrahim led several projects in the areas of knowledge graphs, question answering, knowledge representation, and reasoning. He earned his Ph.D. from KAUST, where his research focused on graph analytics and distributed computing, specifically on building distributed systems to efficiently manage, query, and mine large-scale graphs.

Ibrahim Abdelaziz's research interests included Data Mining over large datasets, Distributed Systems, Machine Learning and Pattern Recognition.

Kai Yi is a PhD candidate in Computer Science at King Abdullah University of Science and Technology (KAUST), supervised by Peter Richtarik and working in the Optimization and Machine Learning Lab. He earned his master’s degree in Computer Science at KAUST in 2021 under the supervision of Mohamed Elhoseiny. He completed his Bachelor of Engineering with honors at Xi’an Jiaotong University (XJTU) in 2019.

He has interned at several leading research institutions, including Sony AI, Vector Institute, Tencent AI Lab, CMU Xulab, NUS CVML Group, and SenseTime Research. His primary research focuses on centralized and federated LLM compression. His work is highly interconnected, featuring significant contributions such as the LLM post-training compression algorithms SymWanda and PV-Tuning (NeurIPS Oral); communication-efficient federated learning methods Cohort-Squeeze (NeurIPS-W Oral), FedP3 (ICLR), and EF-BV (NeurIPS); and multimodal language model projects DACZSL (ICCVW), HGR-Net (ECCV), and VisualGPT (CVPR).

He actively serves as a reviewer for leading journals, including TPAMI, IJCV, and TMC, as well as top conferences such as NeurIPS, ICLR, ICML, CVPR, ECCV, and ICCV.

Kai Yi's primary research interest lies in centralized and federated LLM compression. My work is highly interconnected, featuring significant projects such as the LLM post-training compression algorithms SymWanda and PV-Tuning (NeurIPS Oral), with more on the way; communication-efficient federated learning methods CohortSqueeze (NeurIPS-W Oral), FedP3 (ICLR), and EF-BV (NeurIPS); and multimodal language model projects DACZSL (ICCVW), HGR-Net (ECCV), and VisualGPT (CVPR). His research interests include:

• Machine learning optimization in the large-scale data/model era.
• Conceptual-level knowledge transfer learning: theories and applications.

Specifically, he works on machine learning optimization, federated learning, and zero-shot learning. He is particularly interested in accelerated local training methods and personalized federated learning in data and system heterogeneity. 

He completed his undergraduate studies in Computer Science and Engineering at Sejong University in South Korea. He then pursued a master’s degree in Computer Science at KAUST under the supervision of Prof. Mohamed Elhoseiny, focusing on machine learning and generative models. Building on this foundation, he continued into the PhD program at KAUST, where his research now spans affective vision–language modeling, generative AI, and Neuroscience + AI.

His research focuses on affective vision–language modeling, generative AI, and the integration of Neuroscience with machine learning. He works on multimodal emotion understanding, interpretable generative models, and EEG-based neural signal modeling, aiming to build human-centered AI systems that connect perception, affect, and computational intelligence.

Michał Forystek received his B.Sc. degree in Information and Communication Technology from AGH University of Science and Technology in Kraków, Poland, in 2023.

He has a 2 years of experience as a Java Developer working for IG Group in Kraków.

Currently he is a Computer Science Master's student at the Secure Next Generation Resilient Systems Lab (SENTRY) under the supervision of Professor Charalambos Konstantinou.

Michał's research involves using Load Altering Attacks to exploit the Load Frequency Control in Power Systems and developing the appropriate countermeasures.

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