A professor from EPFL (Lausanne) gave a talk at MBZUAI on computing in the post-Moore era, highlighting the slowing of Moore's Law due to physical limits in transistor miniaturization. He discussed research challenges and opportunities for future computing technologies. He presented examples of post-Moore technologies he helped develop in the datacenter space. Why it matters: As Moore's Law slows, research into alternative computing paradigms becomes critical for the continued advancement of AI and digital services in the UAE and globally.
DERC is partnering with EPFL in Switzerland on a four-year project using EMTR and ML to study electromagnetic disturbance localization in PCBs. Professor Farhad Rachidi (EPFL) and Dr. Nicolas Mora (DERC) will mentor a PhD student. The collaboration builds on prior relationships between DERC researchers and Prof. Rachidi's lab. Why it matters: The partnership strengthens DERC's methodological expertise and international recognition in electromagnetic studies, potentially leading to further collaborations.
TII Chief Researcher Mérouane Debbah and MBZUAI President Eric Xing visited École Polytechnique in France to discuss AI research and training. They reviewed AI projects and opportunities to increase the visibility of UAE-led research. The meeting aimed to strengthen collaboration between MBZUAI, TII, and École Polytechnique. Why it matters: Such partnerships can foster knowledge exchange and accelerate AI innovation in the UAE by leveraging international expertise.
Pascal Fua from EPFL gave a talk at MBZUAI on physics-based deep learning for medical imaging. The talk covered how self-supervision and knowledge of human anatomy and physics can improve deep learning algorithms when training data is limited. Applications discussed included endoscopic heart surgery, colonoscopy, and intubation. Why it matters: This highlights the growing importance of domain knowledge and self-supervision in overcoming data scarcity challenges for AI in healthcare applications within the region.
A KAUST team led by Hossein Fariborzi won second place in the MEMS Design Contest for their "MEMS Resonator for Oscillator, Tunable Filter and Re-Programmable Logic Applications." The device is runtime-reprogrammable, allowing the function of each device in the circuit to be changed during operation. The KAUST team demonstrated that two MEMS resonators could replace over 20 transistors in applications like digital adders, reducing digital circuit complexity. Why it matters: This innovation could significantly reduce power consumption, chip area, and manufacturing costs in microprocessors, advancing the development of energy-efficient microcomputers in the region.
Five Emirati researchers from the Directed Energy Research Center (DERC) concluded a 5-week training course in Switzerland on laser processing and laser-matter interaction at Empa-Swiss Federal Laboratories. The training involved hands-on experience with high-end equipment to conduct independent research. The DERC researchers will contribute to DERC’s projects and help operate its AI-powered laser-matter interaction laboratory. Why it matters: This international training enhances local expertise in advanced laser technology, crucial for developing AI-driven material science capabilities in the UAE.
J. Carlos Santamarina, a Professor of Earth Science and Engineering at KAUST, is researching geomaterial behavior and subsurface processes. His work focuses on energy geo-engineering, resource recovery, and geological storage of energy waste. He uses particle-level experiments, numerical methods, and monitoring systems to understand coupled thermo-hydro-bio-chemo-mechanically processes. Why it matters: This research contributes to energy sustainability and addresses global energy challenges through advanced geotechnology.
Pascal Fua from EPFL presented an approach to implementing convolutional neural nets that output complex 3D surface meshes. The method overcomes limitations in converting implicit representations to explicit surface representations. Applications include single view reconstruction, physically-driven shape optimization, and bio-medical image segmentation. Why it matters: This research advances geometric deep learning by enabling end-to-end trainable models for 3D surface mesh generation, with potential impact on various applications in computer vision and biomedical imaging in the region.