François Fleuret

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Adjunct Professor +41 21 693 40 88

Citizenship: Swiss, French

Birth date: 10.01.1972

François Fleuret got a PhD in Mathematics from INRIA and the University of Paris VI in 2000, and an Habilitation degree in Mathematics from the University of Paris XIII in 2006.

He is Full Professor in the department of Computer Science at the University of Geneva, and Adjunct Professor in the School of Engineering of the École Polytechnique Fédérale de Lausanne. He has published more than 80 papers in peer-reviewed international conferences and journals. 

He is Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence, serves as Area Chair for NeurIPS, AAAI, and ICCV, and in the program committee of many top-tier international conferences in machine learning and computer vision. He is member of the Electrical Engineering Doctoral Program Committee at EPFL, and was or is expert for multiple funding agencies.

He is the inventor of several patents in the field of machine learning, and co-founder of Neural Concept SA, a company specializing in the development and commercialization of deep learning solutions for engineering design. 

His main research interest is machine learning, with a particular focus on computational aspects and small sample learning. 

ELE 136 (Bâtiment ELE)
Station 14
CH-1015 Lausanne

Web site:  Web site:

ELB 110 (Bâtiment ELB)
Station 11
CH-1015 Lausanne

ELB 112 (Bâtiment ELB)
Station 11
CH-1015 Lausanne

Administrative data

Fields of expertise

Statistical machine learning, Deep learning, Pattern recognition


Infoscience publications

Teaching & PhD


Electrical and Electronics Engineering

PhD Programs

Doctoral Program in Electrical Engineering

Doctoral program in computer and communication sciences


Deep learning

The course aims at providing an overview of existing processings and methods, at teaching how to design and train a deep neural network for a given task, and at providing the theoretical basis to go beyond the topics directly seen in the course.

It will touch on the following topics:

  • What is deep learning, introduction to tensors.
  • Basic machine-l