Protecting the systems that power modern life 4 min read · Tue, Apr 14 2026 News Research in Focus cybersecurity Critical Infrastructure Protection Distributed Systems Security AI machine learning The modern world runs on interconnected cyber-physical and distributed digital systems. When cyber-physical systems are compromised, the consequences can extend into the physical world. A cyberattack on a maritime system can disrupt supply chains. A breach in financial infrastructure can destabilize economies. An autonomous system exploit can paralyze transportation networks. These scenarios raise a fundamental question: how can digital and cyber-physical systems be designed to remain secure and resilient in increasingly complex environments? For three decades, KAUST Professor Roberto Di
The Role of Humans in Scientific Discovery in the Age of LLMs — Beyond Asking: Turning LLMs into Research Collaborators Sir Bashir M. Al-Hashimi, Vice President, Research & Innovation, King’s College London (KCL); Distinguished Professor, Department of Engineering, Faculty of Natural, Mathematical & Engineering Sciences, King’s College London (KCL) Apr 8, 12:00 - 14:15 B9 R2325 AI artificial intelligence LLM scientific research scientific knowledge Assistive Technology Rather than offering definitive conclusions, this talk seeks to stimulate dialogue, question assumptions, and inspire new forms of collective thinking about the future of scientific research and doctoral training in an AI-driven world.
What Survives When Code Doesn’t? Dr. Laurent Bindschaedler, Research Group Leader, Max Planck Institute for Software Systems (MPI-SWS) May 4, 12:00 - 13:00 B9 R2325 Trustworthy AI trustworthy machine learning coding AI axplainable AI software development This talk explores how AI-driven code generation shifts the role of software from a durable artifact to a disposable implementation and argues for a new computational model for agentic software that formalizes the fundamental guarantees of intent, state, composition, and effect into explicit, enforceable contracts.
Can AI Make Physicians Better at Diagnosis? Ihsan Ayyub Qazi, Full Professor, Computer Science, Lahore University of Management Sciences (LUMS) Mar 30, 12:00 - 13:00 B9 R2325 AI Trustworthy AI Diagnosis behavioral analysis decision making LLM Biomedical This talk presents a framework for understanding physician-AI collaboration in clinical decision-making, showing that while structured AI literacy training can significantly improve diagnostic accuracy, physicians remain vulnerable to automation bias when LLMs err, highlighting the need to carefully manage human trust and reasoning in AI-assisted clinical decision-making.
Thoughts About Machine Learning Jürgen Schmidhuber, Professor, Computer Science Jan 26, 15:30 - 17:30 B9, Lecture Hall 1, R-2322 AI machine learning deep learning A weekly seminar series from January 26 to April 20, 2026, exploring advanced AI concepts beyond the scope of standard deep learning courses.
AI and Mathematics: Efficient Machine Learning Algorithms Inspired by Dynamical Systems, Complex Analysis, and Embedding Theory Zhihong Xia, Chair Professor, Department of Mathematics, Southern University of Science and Technology Nov 11, 10:30 - 11:30 B9 L3 R3120 AI machine learning PDEs Dynamical Systems scientific computing This talk describes a novel machine learning algorithm, inspired by complex analysis and dynamical systems theory, that significantly improves efficiency in solving partial differential equations and scientific computing, while providing a theoretical framework for reconstructing complex, unknown systems from partial observational data.
