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Administrative data

Nicolas Flammarion

nicolas.flammarion@epfl.ch https://scholar.google.com/citations?user=7nVGYfgAAAAJ&hl

EPFL IC IINFCOM TML
INR 110 (Bâtiment INR)
Station 14
1015 Lausanne

Nicolas Flammarion is a tenure-track assistant professor in computer science at EPFL. Before that, he was a postdoctoral fellow at UC Berkeley, hosted by Michael I. Jordan. He received his PhD in 2017 from Ecole Normale Superieure in Paris, where he was advised by Alexandre d'Aspremont and Francis Bach. In 2018, he received the prize of the Fondation Mathématique Jacques Hadamard for the best PhD thesis in the field of optimization, in 2021, he was one of the recipients of the NeurIPS Outstanding Paper Award, and in 2024, he received a Trust & Safety Google Research Award. His research focuses on learning problems at the intersection of machine learning, statistics, and optimization. He aims to develop algorithmic and theoretical tools that improve our understanding of machine learning and increase its robustness and usability.
Complete CV

Teaching & PhD

PhD Students

Francesco D'Angelo, Hristo Georgiev Papazov, Aditya Vardhan Varre, Gizem Yüce, Oguz Kaan Yüksel

Past EPFL PhD Students

Maksym Andriushchenko (2024), Scott Pesme (2024), Maria-Luiza Vladarean (2024)

Courses

EECS Seminar: Advanced Topics in Machine Learning

ENG-704

Students learn about advanced topics in machine learning, artificial intelligence, optimization, and data science. Students also learn to interact with scientific work, analyze and understand strengths and weaknesses of scientific arguments of both theoretical and experimental results.

Machine learning

CS-433

Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented.

Optimization for machine learning

CS-439

This course teaches an overview of modern optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be discussed in theory and in implementation.