NYU Abu Dhabi hosted a talk by Prof. Debdeep Mukhopadhyay on the intersection of machine learning and hardware security. The talk covered using ML/DL for side-channel attacks, leakage assessment in crypto-devices, and threats to hardware security primitives. Prof. Mukhopadhyay is a visiting professor at NYU Abu Dhabi and Institute Chair Professor at IIT Kharagpur. Why it matters: The talk highlights the growing importance of hardware security in modern systems and the role of machine learning in both attacking and defending hardware vulnerabilities.
A professor from Nanyang Technological University (NTU), Singapore gave a talk at MBZUAI about "Just-Noticeable Difference (JND)" models in visual intelligence. The talk covered visual JND models, research and applications, and future opportunities for JND modeling. JND can help tackle big data challenges with limited resources by focusing on user-centric and green systems. Why it matters: Exploring JND could lead to advancements in AI applications related to visual signal processing, image synthesis, and generative AI in the region.
The KAUST China Alumni Chapter donated anti-pandemic materials, including 2,000 face masks and two Health Guard Robots, to KAUST during the COVID-19 crisis. The donation also included technical advice from alumni. The Hangzhou Association for Science and Technology (HAST) supported the donation of masks. Why it matters: This contribution highlights the strong connection between KAUST and its international alumni network, showcasing their commitment to supporting the university during challenging times.
Keith Ross, Dean of Computer Science, Data Science and Engineering at NYU Shanghai, will be giving a talk on recent advances in Deep Reinforcement Learning (DRL). The talk will review DRL breakthroughs and discuss algorithmic research on DRL for high-dimensional state and action spaces, with applications to robotic locomotion. Ross's research interests include deep reinforcement learning, Internet privacy, peer-to-peer networking, and computer network modeling. Why it matters: Reinforcement learning is a core area of AI research in the GCC region, and a talk by a prominent researcher can help inform and inspire local researchers.
Yanwei Fu from Fudan University will present research on multimodal models, robotic grasping, and fMRI neural decoding. Topics include few-shot learning, object-centered self-supervised learning, image manipulation, and visual-language alignment. The research also covers Transformer compression and applications of large models with MVS 3D modeling in robotic arm grasping. Why it matters: While the talk is not directly about Middle East AI, the topics covered are core to advancing AI research and applications in the region.