Steven Stalder
Data Scientist
Academia
(Alumni)
Steven Stalder joined the SDSC in 2022 as a Data Scientist in the academia team. He received both his BSc and MSc in computer science from ETH Zürich, with a main focus on machine learning and high-performance computing. His first contact with the SDSC was during his master’s thesis, where he worked on explainable neural network models for image classification. Outside of work, Steven loves playing football, reading an interesting book, or watching a good movie.
Projects
deepLNAfrica
In Progress
Deep statistical learning-based image analysis for measurement of socioeconomic development in sub-Saharan Africa using high-resolution satellite images, and geo-referenced household survey data
Energy, Climate & Environment
Publications
Mentioned in
October 31, 2023
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
It is difficult to effectively monitor and track progress in urban housing. We attempt to overcome these limitations by utilizing self-supervised learning with over 15 million street-level images taken between 2008 and 2021 to measure change in London.
September 23, 2022
What you see is what you classify: black box attributions
What you see is what you classify: black box attributions
The lack of transparency of black-box models is a fundamental problem in modern Artificial Intelligence and Machine Learning. This work focuses on how to unbox deep learning models for image classification problems.
Case Studies
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