Pascal Sager

Personal profile

Position at the ZHAW

Scientific Assistant AI/ML | Head of GPU Infrastructure and AI Demonstrators at CAI

www.zhaw.ch/en/engineering/institutes-centres/cai/computer-vision-perception-and-cognition-group/

Expertise and research interests

Deep Learning, Computer Vision, Object Recognition, GPU Computing, ML Engineering

Current research interests: Learning robust representations by incorporating findings from neuroscience | Reasoning | Cognition.

Educational background

- Bachelor in Computer Science, Zurich University of Applied Sciences, 2020
- Advanced Federal Diploma of Higher Education in Electrical Engineering (ISCED 6), 2016

Professional milestones

2018-2020: Software Engineer, Spühl GmbH
2015-2017: Software Engineer PLC, L. Kellenberger & Co. AG
2013-2015: Hardware Design Engineer, Spühl GmbH

Membership of networks

• Datalab, the ZHAW Data Science Laboratory
• CLAIRE, the Confederation of Laboratories for Artificial Intelligence Research in Europe
• data innovation alliance
• CLAIRE Rising Researchers Network
• Digital Futures Lab
• LinkedIn

Projects

• ML-BCA: Machine Learning for Body Composition Analysis / Deputy project leader / Project ongoing
• AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support / Team member / Project completed
• RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Team member / Project completed
• FWA: Visual Food Waste Analysis for Sustainable Kitchens / Team member / Project completed

Publications

• Tuggener, Lukas; Emberger, Raphael; Ghosh, Adhiraj; Sager, Pascal; Satyawan, Yvan Putra; Montoya, Javier; Goldschagg, Simon; Seibold, Florian; Gut, Urs; Ackermann, Philipp; Schmidhuber, Jürgen; Stadelmann, Thilo,

  2023.

  Real world music object recognition.

  Transactions of the International Society for Music Information Retrieval.

  Available from: https://doi.org/10.21256/zhaw-28719

• Sager, Pascal; Salzmann, Sebastian; Burn, Felice; Stadelmann, Thilo,

  2022.

  Unsupervised domain adaptation for vertebrae detection and identification in 3D CT volumes using a domain sanity loss.

  Journal of Imaging.

  8(8), pp. 222.

  Available from: https://doi.org/10.3390/jimaging8080222

• Simmler, Niclas; Sager, Pascal; Andermatt, Philipp; Chavarriaga, Ricardo; Schilling, Frank-Peter; Rosenthal, Matthias; Stadelmann, Thilo,

  2021.

  A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications [paper].

  In:

  Proceedings of the 8th SDS.

  8th Swiss Conference on Data Science, Lucerne, Switzerland, 9 June 2021.

  IEEE.

  pp. 26-31.

  Available from: https://doi.org/10.1109/SDS51136.2021.00012

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