About Anass ElYaagoubi Anass ElYaagoubi Postdoctoral Research Fellow, Statistics Time Series neuroscience data analysis brain signals topological ranking I am a statistician, data scientist, and AI researcher working at the intersection of machine learning, topological data analysis, and complex scientific systems. At King Abdullah University of Science and Technology, I develop new statistical frameworks to uncover hidden structures in brain signals, networks, and high-dimensional biomedical data, while also building AI-powered education technology to make advanced learning more accessible at scale. My work combines mathematics, engineering, and scientific curiosity with a strong focus on real-world impact, from neuroscience research to the future of intelligent education. Events Presented Events Apr 28 - May 4, 2024 Statistical Analysis of Topological Patterns in Dependence Networks of Brain Time Series Data Anass ElYaagoubi, Postdoctoral Research Fellow, Statistics May 2, 11:00 - 12:00 B1 L4 R4102 Given the complex nature of brain signals and the challenges involved in estimating its dependence and analyzing the emerging topological patterns, this dissertation introduces innovative statistical tools designed to explore both the functional and effective connectivity within brain networks. It sheds light on frequency-specific patterns in ADHD subjects and introduces a novel approach for examining the hierarchical structure of brain regions during seizures. Our work provides a novel perspective on the organization of brain networks and presents insight into how various conditions influence their complex structure.
Statistical Analysis of Topological Patterns in Dependence Networks of Brain Time Series Data Anass ElYaagoubi, Postdoctoral Research Fellow, Statistics May 2, 11:00 - 12:00 B1 L4 R4102 Given the complex nature of brain signals and the challenges involved in estimating its dependence and analyzing the emerging topological patterns, this dissertation introduces innovative statistical tools designed to explore both the functional and effective connectivity within brain networks. It sheds light on frequency-specific patterns in ADHD subjects and introduces a novel approach for examining the hierarchical structure of brain regions during seizures. Our work provides a novel perspective on the organization of brain networks and presents insight into how various conditions influence their complex structure.
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