Nicolas Flammarion

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nicolas.flammarion@epfl.ch +41 21 693 27 37 https://scholar.google.com/citations?user=7nVGYfgAAAAJ&hl

EPFL IC IINFCOM TML
INJ 336 (Bâtiment INJ)
Station 14
CH-1015 Lausanne

+41 21 693 27 37
+41 21 693 52 26
Office: INJ 336
EPFL > IC > IINFCOM > TML

EPFL IC IINFCOM MLO
INJ 130 (Bâtiment INJ)
Station 14
CH-1015 Lausanne

+41 21 693 27 37
Office: INJ 130
EPFL > IC > IINFCOM > MLO

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Administrative data

Biography

Nicolas Flammarion is a tenure-track assistant professor in computer science at EPFL. Prior to 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 Mathematique Jacques Hadamard for the best PhD thesis in the field of optimization. His research focuses primarily on learning problems at the interface of machine learning, statistics and optimization.

Teaching & PhD

Teaching

Computer Science

PhD Programs

Doctoral program in computer and communication sciences

PhD Students

Andriushchenko Maksym, Pesme Scott William,

Courses

Machine learning

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

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. Fundamental Contents:

Convexity, Gradient Methods, Proximal algorithms, Stochastic and Online Variants of mentioned methods, Coordinate Descent Meth

EECS Seminar: Advanced Topics in Machine Learning

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.

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