Aude Billard

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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 > IEM > LASA

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

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

EPFL STI IGM-GE
MED 0 1726 (Bâtiment MED)
Station 9
CH-1015 Lausanne

+41 21 693 54 64
Office:  MED 0 1726
EPFL > STI > IGM > IGM-GE

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

+41 21 693 54 64
EPFL > VPA > VPA-AVP-PGE > AVP-PGE-EDOC > EDRS-ENS

+41 21 693 54 64
EPFL > VPA > VPA-FAC > CEAE

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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

Teaching & PhD

Teaching

Microengineering

Mechanical Engineering

PhD Students

Ferreira Duarte Nuno Ricardo, Fichera Bernardo, Gonon David Julian, Gupta Sthithpragya, Huber Lukas, Khurana Harshit, Koptev Mikhail, Liu Yang, Peng Baiyu,

Past EPFL PhD Students

Amanhoud Walid , Batzianoulis Iason , Bombile Michael Bosongo , Calinon Sylvain , De Chambrier Guillaume Pierre Luc , De Souza Ravin Luis , Figueroa Fernandez Nadia Barbara , Gribovskaya Elena , Günter Florent , Hernandez Sanchez Jacob , Hersch Micha , Iwane Fumiaki , Khadivar Farshad , 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 , Yao Kunpeng , 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 practicals can include the following topics:
  • Teaching Robots to Accomplish a Manipulation Task
  • Experimenting with haptic interfaces
  • Controlling a serial robot ABB IRB 120
  • Control of the Micro Delta Direct Drive robot
  • LiniX, linear axis, assembly and control

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

Learning and adaptive control for robots

To cope with constant and unexpected changes in their environment, robots need to adapt their paths rapidly and appropriately without endangering humans. this course presents method to react within millisecunds.

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.