Soufiane Hayou

Soufiane Hayou

Postdoctoral Researcher, UC Berkeley

Soufiane Hayou obtained his PhD in statistics in 2021 from Oxford where he was advised by Arnaud Doucet and Judith Rousseau. He graduated from Ecole Polytechnique in Paris before joining Oxford. During his PhD, he worked mainly on the theory of randomly initialized infinite-width neural networks on topics including the impact of the hyperparameters (variance of the weights, activation function) and the architecture (fully-connected, convolutional, skip connections) on how the 'geometric' information propagates inside the network. He is currently a Peng Tsu Ann Assistant Professor of mathematics at the National University of Singapore.

Program Visits

Modern Paradigms in Generalization, Fall 2024, Research Fellow
program
Modern Paradigms in Generalization
visiting
Fields
Deep learning theory, infinite width/depth limits, data/network compression, efficient ML