Microsoft Azure AI CTO Dr. Xuedong Huang will speak at the MBZUAI Executive Program on AI-powered communications. Huang will share his experience in advancing Microsoft's AI stack, from deep learning infrastructure to new user experiences. He has over 170 U.S. patents and has contributed to speech technology, including Windows SAPI and Azure Speech. Why it matters: This talk can help foster knowledge transfer and collaboration between a global AI leader and the UAE's flagship AI university.
Stanford Professor Yoav Shoham, a leading AI expert, will speak at the MBZUAI Executive Program. Shoham will present on lingual cognition and intelligence as part of a virtual class session. He has founded several AI companies, including AI21 Labs, and chairs the AI Index initiative. Why it matters: The participation of globally recognized AI experts like Shoham enhances the prestige and educational value of AI programs in the UAE, attracting talent and fostering innovation.
Paul Liang from CMU presented on machine learning foundations for multisensory AI, discussing a theoretical framework for modality interactions. The talk covered cross-modal attention and multimodal transformer architectures, and applications in mental health, pathology, and robotics. Liang's research aims to enable AI systems to integrate and learn from diverse real-world sensory modalities. Why it matters: This highlights the growing importance of multimodal AI research and its potential for advancements across various sectors in the region, including healthcare and robotics.
Mausam, head of Yardi School of AI at IIT Delhi and affiliate professor at University of Washington, will discuss Neuro-Symbolic AI. The talk will cover recent research threads with applications in NLP, probabilistic decision-making, and constraint satisfaction. Mausam's research explores neuro-symbolic machine learning, computer vision for radiology, NLP for robotics, multilingual NLP, and intelligent information systems. Why it matters: Neuro-Symbolic AI is gaining importance as it combines the strengths of neural and symbolic approaches, potentially leading to more robust and explainable AI systems.
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.