Aude Billard

photo placeholder image

Full Professor

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

Citizenship : swiss

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

+41 21 693 54 64
+41 21 693 09 39
Office: ME A3 393
EPFL > STI > IMT > LASA

Web site: Web site: https://lasa.epfl.ch/

+41 21 693 54 64
EPFL > STI > STI-SGM > SGM-ENS

+41 21 693 54 64
EPFL > E > E-DOC > EDRS-ENS

+41 21 693 54 64
EPFL > ASSOCIATIONS > 4-CORPS > APEL

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

vCard
Administrative data

Fields of expertise

Machine Learning 
Robotics 
Mechatronics 
Dynamical Systems
Computational Neuroscience

Professional course

Full Professor

EPFL

2013

Associated Professor with Tenure

EPFL

2006-2012

Assistant Professor

EPFL

2002-2006

Assistant Professor

University of Southern California

2000-2002


Education

PhD

Artificial Intelligence

University of Edinburgh

1998

MSc

Knowledge-Based Systems

University of Edinburgh

1996

BSc & MSc

Physics

EPFL

1995


Awards

Intel Corporation

SNSF Professeur Boursier

The Outstanding Young Person in Science and Innovation

Teaching & PhD

Teaching

Microengineering

Mechanical Engineering

PhD Programs

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, Gaspar Pinto Ramos Correia Carolina, Gonon David Julian, Haget Anaïs Betsabeh, Hernandez Sanchez Jacob, Huber Lukas, Iwane Fumiaki, Khadivar Farshad, Khurana Harshit, Koptev Mikhail, Yao Kunpeng,

Past EPFL 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, highly noisy and multi dimensional data

Advanced machine learning

This course will present some of the core advanced methods in the field for structure discovery, classification and non-linear regression. This is an advanced class in Machine Learning; hence, students are expected to have some background in the field.