Lukas Tuggener

ZHAW School of Engineering
Centre for Artificial Intelligence
Technikumstrasse 71
8400 Winterthur

+41 (0) 58 934 62 04
lukas.tuggener@zhaw.ch

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

Projekte

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

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.

Verfügbar unter: https://doi.org/10.21256/zhaw-28719
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

Konferenzbeiträge, peer-reviewed

Emberger, Raphael; Boss, Jens Michael; Baumann, Daniel; Seric, Marko; Huo, Shufan; Tuggener, Lukas; Keller, Emanuela; Stadelmann, Thilo,

2023.

Video object detection for privacy-preserving patient monitoring in intensive care [Paper].

In:

2023 10th IEEE Swiss Conference on Data Science (SDS).

10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023.

IEEE.

S. 85-88.

Verfügbar unter: https://doi.org/10.1109/SDS57534.2023.00019
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,

2021.

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

In:

2020 25th International Conference on Pattern Recognition (ICPR).

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

IEEE.

S. 9188-9195.

Verfügbar unter: https://doi.org/10.1109/ICPR48806.2021.9412290
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/10.1109/SDS.2019.00-11
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:

Artificial Neural Networks in Pattern Recognition.

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

Springer.

S. 17-38.

Lecture Notes in Computer Science ; 11081.

Verfügbar unter: https://doi.org/10.1007/978-3-319-99978-4_2
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:

2018 24th International Conference on Pattern Recognition (ICPR).

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

IEEE.

S. 1-6.

Verfügbar unter: https://doi.org/10.1109/ICPR.2018.8545307

Weitere Publikationen

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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