Presenter

Quentin Petit is a postdoctoral researcher at Mines Paris – PSL University, within the Computer Science Research Center. He obtained his PhD from Université Paris-Saclay, where his thesis investigated efficient methods for distributed sparse data processing and embedding pre-processing at scale. His research lies at the intersection of high-performance computing, large-scale deep learning, embedding models, and sparse linear algebra. He is particularly interested in reducing the complexity of training deep models by leveraging spectral methods to preserve and extract high-quality data, thereby enabling more efficient and scalable learning.