Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.
KAUST Associate Professor Taous-Meriem Laleg-Kirati leads the Estimation, Modeling and ANalysis (EMAN) research group, focusing on control theory, system modeling, and signal applications. Her group develops mathematical models and algorithms to control processes relying on real-time feedback, especially for systems where experimental data is limited. The EMAN group recently developed a real-time control algorithm for a solar membrane distillation system, increasing water production by over 50% in simulations. Why it matters: Laleg-Kirati's work advances both engineering and healthcare by combining model-based research with AI, offering opportunities for personalized medicine and efficient resource management in the region.
KAUST recently hosted the European Embedded Control Institute's International Graduate School on Control (IGSC). As part of the event, KAUST Professor Jeff Shamma gave a one-week course on "Game Theory and Distributed Control". The course had over 30 registered attendees, including participants from KAUST, KACST, King Saud University, and nine European universities. Why it matters: Hosting international events like IGSC enhances KAUST's global reputation, fosters collaboration opportunities, and exposes visiting researchers to KAUST's research environment.
This paper introduces a longitudinal control system for autonomous racing vehicles with combustion engines, translating trajectory-tracking commands into low-level vehicle controls like throttle, brake pressure, and gear selection. The modular design facilitates integration with various trajectory-tracking algorithms and vehicles. Experimental validation on the EAV24 racecar during the Abu Dhabi Autonomous Racing League at Yas Marina Circuit demonstrated the system's effectiveness, achieving longitudinal accelerations up to 25 m/s². Why it matters: This research contributes to the advancement of autonomous racing technology in the region, showcasing practical applications in high-performance scenarios and fostering innovation in vehicle control systems.
KAUST Professor Jeff Shamma has been elected as a fellow of the International Federation of Automatic Control (IFAC). IFAC is a multinational federation dedicated to the advancement of control engineering. Shamma is a professor of electrical and computer engineering at KAUST. Why it matters: This recognition highlights KAUST's contributions to the field of control engineering and the university's growing international reputation.
Munther Dahleh, director at the MIT Institute for Data, Systems, and Society (IDSS), discussed his group's research on network systems at the KAUST 2018 Winter Enrichment Program. The research focuses on the fragility of large networked systems, like highway systems, in response to disruptions that may lead to catastrophic failures. Dahleh's team studies transportation networks, electrical grids, and financial markets to understand system interconnection in causing systemic risk. Why it matters: Understanding networked systems is crucial for building resilient infrastructure and mitigating risks in critical sectors across the GCC region.
A talk introduces a computational framework for learning a compact structured representation for real-world datasets, that is both discriminative and generative. It proposes to learn a closed-loop transcription between the distribution of a high-dimensional multi-class dataset and an arrangement of multiple independent subspaces, known as a linear discriminative representation (LDR). The optimality of the closed-loop transcription can be characterized in closed-form by an information-theoretic measure known as the rate reduction. Why it matters: The framework unifies concepts and benefits of auto-encoding and GAN and generalizes them to the settings of learning a both discriminative and generative representation for multi-class visual data.
This paper introduces rational counterfactuals, a method for identifying counterfactuals that maximize the attainment of a desired consequent. The approach aims to identify the antecedent that leads to a specific outcome for rational decision-making. The theory is applied to identify variable values that contribute to peace, such as Allies, Contingency, Distance, Major Power, Capability, Democracy, and Economic Interdependency. Why it matters: The research provides a framework for analyzing and promoting conditions conducive to peace using counterfactual reasoning.