Olivier Canévet
EPFL STI IEM LIDIAP
ELD 241 (Bâtiment ELD)
Station 11
1015 Lausanne
Website: https://idiap.epfl.ch/
EPFL › VPA › VPA-AVP-DLE › AVP-DLE-EDOC › EDEE-ENS
Expertise
Computer Vision, Machine Learning, Deep Learning
Research engineer at the Idiap Research Institute.
I am a research engineer at the Idiap Research Institute. I have a PhD from the école Polytechnique Fédérale de Lausanne done
under the supervision of Dr François Fleuret. My research was funded by the Swiss National Science Foundation (DASH). I graduated from the French Engineering School TELECOM Bretagne (Brest) in 2012 and hold a Master in image processing from Université de Rennes1. I did a six-month internship at the French Space Agency (CNES) working on very high resolution satellite images. I was also a technical student at CERN and worked on a ranking method for Invenio. I spent one year on Amsterdam Island (Indian Ocean) where I was in charge of the seismological and geomagnetic observatories of école et Observatoire des Sciences de la Terre de Strasbourg (EOST) affiliated with the French Polar Institut (IPEV).
under the supervision of Dr François Fleuret. My research was funded by the Swiss National Science Foundation (DASH). I graduated from the French Engineering School TELECOM Bretagne (Brest) in 2012 and hold a Master in image processing from Université de Rennes1. I did a six-month internship at the French Space Agency (CNES) working on very high resolution satellite images. I was also a technical student at CERN and worked on a ranking method for Invenio. I spent one year on Amsterdam Island (Indian Ocean) where I was in charge of the seismological and geomagnetic observatories of école et Observatoire des Sciences de la Terre de Strasbourg (EOST) affiliated with the French Polar Institut (IPEV).
Education
PhD in Electrical Engineering
|2012 – 2016 EPFL
Engineering diploma
|2007 – 2012 TELECOM Bretagne
Teaching & PhD
Courses
Machine Learning for Engineers
EE-613
The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done in python using jupyter notebooks.