Welcome to my academic page! I am Lionel Yelibi, a data science researcher specializing in clustering methods for high-dimensional and time-series data. My expertise lies in developing algorithms that leverage complex systems and spin models, originally from physics, to analyze intricate datasets, including financial market data. I am particularly interested in creating efficient, scalable clustering methods, such as the Agglomerative Fast Super-Paramagnetic Clustering (ALC) technique, designed to identify and map hidden structures within interdependent data points.

My professional interests are at the intersection of computational modeling and real-world data applications, especially in finance, where clustering helps to detect relationships among stock behaviors and market dynamics. In my academic research, I explore how advanced clustering algorithms can improve pattern recognition and data interpretation in environments with high connectivity and complexity, striving to push the boundaries of what data science can reveal.