KAUST Research Scientist Abla Kammoun received the IEEE Wireless Communication Letters (WCL) Top Editor Award for contributions to the review process. Kammoun's research focuses on random matrix theory, wireless communication systems, signal processing, big data, and machine learning. She joined the WCL editorial board in 2015 and was recognized for ensuring a fast, fair, and valuable review process. Why it matters: The award highlights KAUST's contributions to advancing wireless communication technologies and recognizes the important role of peer review in maintaining quality in the field.
This talk explores modern machine learning through high-dimensional statistics, using random matrix theory to analyze learning models. The speaker, Denny Wu from University of Toronto and the Vector Institute, presents two examples: hyperparameter selection in overparameterized models and gradient-based representation learning in neural networks. The analysis reveals insights such as the possibility of negative optimal ridge penalty and the advantages of feature learning over random features. Why it matters: This research provides a deeper theoretical understanding of deep learning phenomena, with potential implications for optimizing training and improving model performance in the region.
This talk discusses the asymptotic study of large asymmetric spiked tensor models. It explores connections between these models and equivalent random matrices constructed through contractions of the original tensor. Mohamed El Amine Seddik, currently a senior researcher at TII in Abu Dhabi, presented the work. Why it matters: The research provides theoretical foundations relevant to machine learning algorithms that leverage low-rank tensor structures, potentially impacting AI research and applications in the region.