Aude Billard

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Full Professor

aude.billard@epfl.ch +41 21 693 54 64 http://lasa.epfl.ch/

EPFL STI IGM-GE
ME A3 393 (Bâtiment ME)
Station 9
CH-1015 Lausanne

+41 21 693 54 64
Unit: IGM-GE
Office: ME A3 393

EPFL STI IMT LASA
ME A3 393 (Bâtiment ME)
Station 9
CH-1015 Lausanne

+41 21 693 54 64
+41 21 693 09 39
Web site: https://lasa.epfl.ch/
Unit: LASA
Office: ME A3 393

Web site: https://apel.epfl.ch/
Unit: APEL

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Administrative data

Fields of expertise

Machine Learning
Neural Modeling
Humanoid Robotics
Human-Robot Interactions
Computational Neuroscience

Teaching & PhD

Teaching

Microengineering,
Mechanical Engineering

PhD Programs

Doctoral program in computer and communication sciences
Doctoral Program in Manufacturing Systems and Robotics
Doctoral Program in Electrical Engineering
Doctoral program in robotics, control, and intelligent systems
Doctoral Program in Neuroscience
Doctoral Program Digital Humanities
Doctoral Program in Biotechnology and Bioengineering

PhD Students

Amanhoud Walid, Aryan Saurav, Bombile Bosongo Michael, Ferreira Duarte Nuno Ricardo, Fichera Bernardo, Gonon David Julian, Haget Anaïs Betsabeh, Hernandez Sanchez Jacob, Huber Lukas, Iwane Fumiaki, Khadivar Farshad, Koptev Mikhail, Yao Kunpeng,

Past PhD Students

Batzianoulis Iason , Calinon Sylvain , De Chambrier Guillaume Pierre Luc , De Souza Ravin Luis , Figueroa Fernandez Nadia Barbara , Gribovskaya Elena , Günter Florent , Hersch Micha , Khansari Zadeh Seyed Mohammad , Khoramshahi Mahdi , Kim Seungsu , Kronander Klas Jonas Alfred , Li Miao , Lukic Luka , Mirrazavi Salehian Seyed Sina , Noris Basilio , Petreska von Ritter-Zahony Biljana , Sauser Eric , Shukla Ashwini , Sommer Nicolas , Ureche Ana-Lucia , Yin Hang ,

Courses

Machine learning programming

This is a practice-based course, where students program algorithms in machine learning and evaluate the performance of the algorithm thoroughly using real-world dataset.

Robotics practicals

The goal of this lab series is to practice the various theoretical frameworks acquired in the courses on a variety of robots, ranging from industrial robots to autonomous mobile robots, to robotic devices, all the way to interactive robots.

Applied machine learning

Real-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear,...

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