I am an improver when facing existing systems and an efficient deployer when starting from scratch. Specialized in deep learning for image recognition and computer vision, I adapt very quickly to new environments, produce fast prototypes and then perfect them. I enjoy sharing knowledge with others and presenting exciting new results.
Experience
| 2018 – ongoing | Research Engineer at ams AG |
| 2017 – 2018 | Applied Research Fellow – Image Classification Specialist at WIPO |
| 2017 | Machine Learning / Data Analytics Master thesis at Technis SA |
| 2015 & 2016 | Teaching-assistant, ICC course at EPFL |
| 2014 & 2014 | Teaching-assistant, Digital Systems course at EPFL |
Education
| 2017 | M.Sc. in Computer Science, EPFL |
| 2016 | B.Sc. in Computer Science, EPFL |
| 2012 | Maturité (applied maths/physics), Lycée-Collège de Saint-Maurice |
Technical skills
Machine Learning / Deep Learning
Mastery of Python, using high-level frameworks (PyTorch, Keras, NumPy, some Tensorflow background).
Object-Oriented Programming
Very good knowledge of Java, with additional notions of C++ and C#.
Others
Good knowledge of Matlab, particularly in Image Processing and Computer Vision
System-oriented programming (C, Perl, Bash/Shell), experience in mobile programming (Android), notions of Scala
High familiarity with Linux (Ubuntu/Debian, RedHat), Windows, and macOS
Self-taught knowledge of image, video and music editing (Adobe Photoshop/Lightroom, Sony Vegas, Propellerhead Reason, Steinberg Cubase)
Basic familiarity with 3D editing software (Blender, Unity) and computer graphics (OpenGL)
Everyday usage of control version systems (Git, Subversion)
Projects highlights
Smart flooring data analytics (2017, Master Thesis at Technis SA)
Implementation of rolling objects counting and accuracy improvement for counting with static people, applied to smart flooring technology developed by Technis SA, using Machine Learning and Computer Vision algorithms.
Depth-map fusion (2016 semester project)
Alignment and fusion of point clouds captured with different points of view to recover loss from occlusion and increase resolution, applicable to structured light systems.
3D Fruit Ninja (2016)
Immersive Android game for VR on mobile devices (Google Cardboard) with Kinect, using Unity, Virtual Reality course, EPFL.
Smartphone lens distortion correction (2016)
Implementation of a software-based correction for lens distortion in smartphones, Color Reproduction course, EPFL.
High-definition and depth from a single image (2016)
Recover both a high-definition image and a depth map from a single image, using a camera mounted with a coded aperture, Computational Photography course, EPFL.
Online courses and self-learned topics
Machine Learning – Stanford University (Coursera, certificate available, grade: 100%)
HTML, CSS, Javascript (Various websites)
Languages
| French | Native language |
| English | Fluent |