Aude Billard
41 21 693 54 64
EPFL
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SMT-ENS
41 21 693 54 64
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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
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IEM
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LASA
Web site: Web site: https://lasa.epfl.ch/
41 21 693 54 64
EPFL
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SGM-ENS
Web site: Web site: https://apel.epfl.ch/
Fields of expertise
Robotics
Mechatronics
Dynamical Systems
Computational Neuroscience
Awards & Honors
- IEEE Fellow
- ERC Advanced Grant
- Member of the Swiss Academy of Engineering Sciences (SATW)
- Best Reviewer Award, IEEE Robotics and Automation Society
- The Outstanding Young Person in Science and Innovation, Junior Chamber of Commerce, Switzerland
- Innovative Teaching Grant , Intel Corporation
- AAAI Conference on Artificial Intelligence, New York, February 10, 2020
- Conference on Machine Learning, ICML, Long Beach, USA
- 11th IFAC Symposium on Nonlinear Control Systems, Vienna, Austria
- 12th Progress in Motor Control Conference, Amsterdam, Netherlands
- 10th International Conference on Intelligent Robotics and Applications, Wuhan, China
- 11th ACM/IEEE International Conference on Human-Robot Interaction, Christchurch, NZ
- IEEE International Conference on Humanoids, Seoul, Korea
- IEEE-RAS International Conference on Robotics and Automation, Karlsruhe, Germany
- IEEE Symposium on Human-Robot Interactive Communication, Nashville, USA
Best Paper Awards:
- Best Student Paper Award, Robotics Science and Systems, (RSS)
- Finalist Best Overall Conference Paper Award, Robotics Science and Systems, (RSS)
- King-Sun Fu Best Transactions Paper Award, IEEE & Robotics and Automation Society
- Best Cognitive Robotics Paper Award, Int. Conf. on Robotics and Automation (ICRA)
- JTSC Novel Technology Best Paper Award, IEEE Int. Conf. Intellig. & Robotics Syst. (IROS)
- Best Paper Award, IEEE Symposium on Human-Robot Interaction (ROMAN)
- Best Paper Award, Workshop on Universal Access and Assistive Technology (CWUATT)
- Finalist for Best Paper Award, Neural Information Processing Symposium
- Finalist for Best Paper Award, IEEE Int. Conf. on Humanoid Robots
Current work
Research Council Member, Swiss National Science Foundation
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
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, Liu Yang, 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 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 adaptative 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
- Introduction to the major mathematical principles of Machine Learning
- Structure Discovery: spectral and kernel methods, kernel PCA.CCA, kernel K-means, Spectral Clustering, Manifold Learning, Support Vector Clustering
- Advanced Classification and Nonlinear Regression Methods: nu-SVM/SVR, Relevance Vector Machine, Transductive SVM, Gaussian Processes,
- Stochastic