LLM

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

Peter Richtarik, Professor, Computer Science

Sep 15, 12:00 - 13:00

B9 L2 R2325

AI machine learning optimization algorithms LLM

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.

Muhammad Samiullah

Intern, Cyber Security and Resilience Technology

cybersecurity ML FPGA LLM

Cyber Security, FPGA security, AI/ML security

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

1 min read · Tue, Apr 22 2025

AI Generative AI and LLMs LLM

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

Ibrahim Abdelaziz, Senior Research Scientist, IBM

Apr 13, 16:00 - 17:00

B2/3 L0 R0215

LLM machine learning artificial intelligence AI

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.

Juexiao Zhou

Ph.D. Student, Computer Science

Deep learning Medical imaging Security and Privacy LLM

Ines Pinto Gouveia

Postdoctoral Research Fellow, Cyber Security and Resilience Technology

resilience Fault-Tolerant Computing intrusion tolerance cybersecurity FPGA LLM

Working towards building safe, secure and resilient systems.