S. Jenni, and P. Favaro (2018). "Self-Supervised Feature Learning by Learning to Spot Artifacts." CVPR 2018.
BSc in Computer Science - University of Bern, 2015
With minors in Mathematics (60 ECTS) and Physics (30 ECTS) – magna cum laude
Thesis Title: A Study of 3D Deformable Parts Models for Detection and Pose-Estimation
MSc in Computer Science - University of Bern, 2017
With specialization in Advanced Information Processing – summa cum laude
Thesis Title: From Cartoons to Real Images: An Approach to Unsupervised Visual Representation Learning
PhD in Computer Science - University of Bern, 2017-present
Topics: Unsupervised learning methods with a focus on self-supervised feature learning
Junior Data Analyst - Philip Morris International, 2016
I developed a Matlab tool for the automatic analysis of ciliary beating movies. The tool allows the extraction of key endpoints such as tissue activity and main beating frequencies with a higher accuracy compared to previous methods. By tracking tissue patches over different videos it also allows the analysis of regions over a longer period of time.
Software Engineering Intern - AdNovum, 2015
I worked on a mobile payment app, implementing several parts of the iOS version of the app in Objective-C. My main responsibilities were: Feature implementation, bug-fixing and Proof of Concept. The work took place in large teams and was managed with Scrum and the use of tools such as Jira and Confluence.
Joint Alumni Association in Computer Science (JAACS):
Award for the best Master Thesis in Computer Science (2017)
P.A.I.S.S. Best Poster Award:
Award for the poster on “Self-Supervised Feature Learning by Learning to Spot Artifacts” (2018)
Python, MATLAB/Octave, Java, Objective-C, LaTeX
German (native), English (fluent), French (proficient)
S. Jenni, and P. Favaro (2018). "Deep Bilevel Learning." ECCV 2018.
S. Jenni, and P. Favaro (2019). "On Stabilizing Generative Adversarial Training with Noise." CVPR 2019.
EEG-based Outcome Prediction after Cardiac Arrest with Convolutional Neural Networks: Performance and Visualization of Discriminative Features
S. Jonas, A. Rossetti, M. Oddo, S. Jenni, P. Favaro and F. Zubler (2019). "EEG-based Outcome Prediction after Cardiac Arrest with Convolutional Neural Networks: Performance and Visualization of Discriminative Features." HBM 2019.