Nicolas Makaroff
Nicolas holds a Ph.D. in applied mathematics from Université Paris Dauphine - PSL, where his research focused on machine learning, with particular emphasis on attention mechanisms and geodesic approaches to segmentation. His work on designing advanced deep learning architectures for complex datasets has led to multiple publications at leading international conferences.
He brings hands-on expertise in self-supervised learning and large-scale optimisation, and is currently contributing to Neuralk's mission to develop the first enterprise tabular foundation model.
Session
This hands-on tutorial takes participants from zero to confident use of tabular foundation models. Using real datasets, we will run TabICL-style models, benchmark them rigorously against XGBoost and Random Forest, diagnose their behavior, and build intuition for when they help and when they don't.