Lukas Tuggener

Persönliches Profil

Tätigkeit an der ZHAW

Doktorand, DIZH Fellow

www.zhaw.ch/de/engineering/institute-zentren/init/information-engineering/kuenstliche-intelligenz/

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Data Science, Data Mining, Maschinelles Lernen, Deep Learning, Statistisches Modellieren

Aus- und Fortbildung

Informatikmittelschule, Kantonsschule Büelrain
BSc Wirtschaftsingenieurwesen, ZHAW Winterthur - Vertiefungsrichtung Wirtschaftsmathematik
MSc Statistik, ETH Zürich - Vertiefungsrichtung Computational ­Statistics

Beruflicher Werdegang

Wissenschaftlicher Assistent, ZHAW - Institut für Datenanalyse und Prozessdesign
Praktikum als Softwareentwickler, UBS

Mitglied in Netzwerken

• ETH Alumni
• Datalab, the ZHAW Data Science Laboratory

Projekte

• Practical data efficient deep learning trough contrastive self-supervised learning / ProjektleiterIn / Projekt laufend
• OSR4H – Open Set Recognition for Hematology / Teammitglied / Projekt laufend
• RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Teammitglied / Projekt abgeschlossen
• Ada – Advanced Algorithms for an Artificial Data Analyst / Teammitglied / Projekt abgeschlossen
• Libra: A One-Tool Solution for MLD4 Compliance / Teammitglied / Projekt abgeschlossen
• DeepScore: Digitales Notenpult mit musikalischem Verständnis durch Active Sheet Technologie / Teammitglied / Projekt abgeschlossen

Publikationen

• Tuggener, Lukas; Schmidhuber, Jürgen; Stadelmann, Thilo,

  2022.

  Is it enough to optimize CNN architectures on ImageNet?.

  Frontiers in Computer Science.

  4(1041703).

  Verfügbar unter: https://doi.org/10.3389/fcomp.2022.1041703

• Tuggener, Lukas; Amirian, Mohammadreza; Benites de Azevedo e Souza, Fernando; von Däniken, Pius; Gupta, Prakhar; Schilling, Frank-Peter; Stadelmann, Thilo,

  2020.

  Design patterns for resource-constrained automated deep-learning methods.

  AI.

  1(4), S. 510-538.

  Verfügbar unter: https://doi.org/10.3390/ai1040031

• Amirian, Mohammadreza; Tuggener, Lukas; Chavarriaga, Ricardo; Satyawan, Yvan Putra; Schilling, Frank-Peter; Schwenker, Friedhelm; Stadelmann, Thilo,

  2021.

  Two to trust : AutoML for safe modelling and interpretable deep learning for robustness [Paper].

  In:

  Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020.

  1st TAILOR Workshop on Trustworthy AI at ECAI 2020, Santiago de Compostela, Spain, 29-30 August 2020.

  Springer.

  Verfügbar unter: https://doi.org/10.21256/zhaw-22061

• Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen; Stadelmann, Thilo,

  2020.

  The DeepScoresV2 dataset and benchmark for music object detection [Paper].

  In:

  Proceedings of the 25th International Conference on Pattern Recognition 2020 (ICPR’20).

  25th International Conference on Pattern Recognition 2020 (ICPR’20), Online, 10-15 January 2021.

  IAPR.

  Verfügbar unter: https://doi.org/10.21256/zhaw-20647

• Tuggener, Lukas; Amirian, Mohammadreza; Rombach, Katharina; Lörwald, Stefan; Varlet, Anastasia; Westermann, Christian; Stadelmann, Thilo,

  2019.

  Automated machine learning in practice : state of the art and recent results [Paper].

  In:

  2019 6th Swiss Conference on Data Science (SDS).

  6th Swiss Conference on Data Science (SDS), Bern, 14. Juni 2019.

  IEEE.

  S. 31-36.

  Verfügbar unter: https://doi.org/10.21256/zhaw-3156

• Elezi, Ismail; Tuggener, Lukas; Pelillo, Marcello; Stadelmann, Thilo,

  2018.

  DeepScores and Deep Watershed Detection : current state and open issues [Paper].

  In:

  Proceedings of the 1st International Workshop on Reading Music Systems.

  1st International Workshop on Reading Music Systems at ISMIR 2018, Paris, France, 20 September 2018.

  Paris:

  Society for Music Information Retrieval.

  S. 13-14.

  Verfügbar unter: https://doi.org/10.21256/zhaw-4777

• Stadelmann, Thilo; Amirian, Mohammadreza; Arabaci, Ismail; Arnold, Marek; Duivesteijn, Gilbert François; Elezi, Ismail; Geiger, Melanie; Lörwald, Stefan; Meier, Benjamin Bruno; Rombach, Katharina; Tuggener, Lukas,

  2018.

  Deep learning in the wild [Paper].

  In:

  Proceedings of the 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR).

  8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR), Siena, Italy, 19-21 September 2018.

  IAPR.

  Verfügbar unter: https://doi.org/10.21256/zhaw-3872

• Tuggener, Lukas; Elezi, Ismail; Schmidhuber, Jürgen; Stadelmann, Thilo,

  2018.

  Deep watershed detector for music object recognition [Paper].

  In:

  Proceedings of the 19th International Society for Music Information Retrieval Conference.

  19th International Society for Music Information Retrieval Conference, Paris, 23-27 September 2018.

  Paris:

  Society for Music Information Retrieval.

  Verfügbar unter: https://doi.org/10.21256/zhaw-3760

• Tuggener, Lukas; Elezi, Ismail; Schmidhuber, Jürgen; Pelillo, Marcello; Stadelmann, Thilo,

  2018.

  DeepScores : a dataset for segmentation, detection and classification of tiny objects [Paper].

  In:

  Proceedings of the 24th International Conference on Pattern Recognition.

  24th International Conference on Pattern Recognition (ICPR 2018), Beijing, China, 20-28 August 2018.

  Beijing:

  IAPR.

  S. 1-6.

  Verfügbar unter: https://doi.org/10.21256/zhaw-4255

• Amirian, Mohammadreza; Rombach, Katharina; Tuggener, Lukas; Schilling, Frank-Peter; Stadelmann, Thilo,

  2019.

  Efficient deep CNNs for cross-modal automated computer vision under time and space constraints [Paper].

  In:

  ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019.

  ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

  Verfügbar unter: https://doi.org/10.21256/zhaw-18357

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