AI

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs

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B9 L2 R2325

This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers.

Bridging AI and Spatial Biology to Navigate Biological Landscapes

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This thesis presents several studies by proposing AI-driven methods to supply a comprehensive and precise representation of biological landscapes via spatially resolved transcriptomics (SRT) data.

SAAI for Kids hackathon introduces Saudi youth to creative uses of AI

3 min read ·
More than 200 students joined KAUST’s SAAI Hackathon to explore AI in art, music and design

AI for Chips and Chips for AI

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B9 L2 R2325

This seminar presents a unified "AI for Chips & Chips for AI" approach, demonstrating how AI enhances semiconductor design while specialized silicon accelerates AI computation, creating a rapid innovation cycle.

Rising stars, global impact: KSA at the forefront of AI innovation

1 min read ·
KAUST showcases global AI talent to power Saudi Arabia’s rise as a hub for innovation and leadership in artificial intelligence.

Making LLMs Agentic: How Tool Use and Synthetic Data Drive Real-World Intelligence

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B2/3 L0 R0215

This talk presents advancements in agentic AI, focusing on creating and utilizing datasets and models that enable Large Language Models to effectively interact with external tools for real-world applications.
Rising Stars in AI" Symposium 2025

Rising Stars in AI Symposium 2025

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Building 19, Halls 1, 2 and 3

AI

Join KAUST's Rising Stars in AI Symposium 2025, bringing together exceptional emerging talent shaping the future of AI research and innovation.

KAUST developing robotic system to improve date palm harvesting

1 min read ·
Combination of AI and robotics positions Saudi Arabia to transform date farming.
kaust-iit-collaboration-life-sciences-nanotechnology-research

KAUST collaboration to benefit life sciences and nanotechnology research

1 min read ·
KAUST and IIT unite to enhance research in life sciences, AI, robotics and nanotech, driving innovation through collaboration.
optoelectronic device can adapt to light

Artificial neurons enable neuromorphic computing with light

1 min read ·
An adaptive capacitor that can sense and learn through optical stimuli enables learning and recognition tasks.
AI tool maps hidden links between diseases

AI tool maps hidden links between diseases

1 min read ·
Understanding how diseases trigger one another could lead to better treatments, prevention strategies and improved genetic risk prediction.
Olga Krestinskaya

International recognition for KAUST Ph.D. candidate Olga Krestinskaya

3 min read ·
Olga Krestinskaya, a KAUST Ph.D. student, wins Best Poster at the 2024 Nature Neuromorphic Computing Conference for her work on energy-efficient AI hardware.
Sustainable solutions in water conservation

KAUST-Engineered tool drives sustainable solutions in water conservation

3 min read ·
KAUST’s startup EcoStream cuts water waste by 50% while promoting affordability and sustainability, winning the 2024 StartSmart prize.

Aznaur Aliev

M.S. Student

Mhd. Emad Alolaby

Mohammed A Shukri

Technical Consultant at KAUST (King Abdullah University of Science and Technology).

Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

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B3 L5 R5209

Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems

Omar Khalifa

PhD student in the Electrical Engineering Department, CEMSE, King Abdullah University of Science and Technology (KAUST) ,working under the supervision of Prof. Tareq Al-Naffouri.

Hamzeh Abu Qamar

MS Student, Electrical, Electronics and Communications Engineering at King Abdullah University of Science and Technology (KAUST).