Prof. Dr. Thilo Stadelmann

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

+41 (0) 58 934 72 08
thilo.stadelmann@zhaw.ch

Persönliches Profil

Leitungsfunktion

Zentrumsleitung Centre for Artificial Intelligence
Leitung Computer Vision, Perception and Cognition Group

Tätigkeit an der ZHAW

Wissenschaftler, Dozent, Redner, Consultant, Networker, Projektleiter, Reviewer, Mentor, Leiter, Vater, Glaubender.

Rollen:

Leiter Forschungszentrum CAI: www.zhaw.ch/de/engineering/institute-zentren/cai/
Professor für AI/ML (Professur für Engineering ‒ Information): stdm.github.io/research/
Leiter Computer Vision, Perception and Cognition Group: www.zhaw.ch/de/engineering/institute-zentren/cai/computer-vision-perception-and-cognition-group/
Keynote Redner, Premium Speakers: premium-speakers.com/referent-moderator/thilo-stadelmann/

Aktivitäten:

Vorträge und Beiträge zur wissenschaftlichen Community: stdm.github.io/service/
ZHAW staff prayer meeting: stdm.github.io/prayer

Preise:

ZHAW digital Impact Award 2022
DIZH Fellowship 2022
Swiss Data Science Conference Best Paper Award 2021
Swiss Data Science Conference Best Poster Presentation Award 2020
ZHAW Lehrpreis "Best Teaching - Best Practices" (3. Platz) 2019

stdm.github.io

Lehrtätigkeit in der Weiterbildung

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Künstliche Intelligenz und maschinelles Lernen: Deep Learning, Mustererkennung, Computer Vision, Audioanalyse, intelligente Systeme.

Aktuelle Forschungsinteressen: Robustes und effizientes Deep Learning in neuronalen Netzen für praktische Anwendungsfälle, Reinforcement Learning für autonome Systeme, generellere und biologisch inspirierte künstliche Intelligenz, Large Language Models; ethische und gesellschaftliche Aspekte solcher Systeme.

Siehe auch: stdm.github.io/research/

Aus- und Fortbildung

Führungsausbildung ZHAW, 2018, Zürcher Hochschule für Angewandte Wissenschaften
CAS Hochschuldidaktik, 2015, Pädagogische Hochschule Zürich
Promotion, Dr. rer. nat., 2010, Philipps-Universität Marburg
Studium der Informatik, Dipl. Inform. (FH), 2004, Fachhochschule Giessen

Beruflicher Werdegang

2023-heute: Founder und Verwaltungsrat, AlpineAI AG
2023-heute: Keynote Redner, Premium Speakers Agency
2021-heute: Professor für AI/ML, Leiter ZHAW CAI, Leiter CVPC Group
2019-2021: Wissenschaftlicher Leiter, ZHAW digital
2018-2021: Professor für Informatik, ZHAW InIT
2017-2018: Managing Director ad Interim, Swiss Alliance for Data-Intensive Services
2016-heute: Board der data innovation alliance
2015-2021: Stv. Leitung Schwerpunkt Information Engineering, ZHAW
2015-2017: Co-Organisator Zürich Machine Learning & Data Science Meetup
2014-2018: Vizepräsidet SGAICO, Swiss Group of Artificial Intelligence and Cognitive Science
2013-2019: Leiter, ZHAW Datalab
2013-heute: Co-Gründer und Mitglied des Vorstands, ZHAW Datalab
2013-2018: Dozent Information Engineering (50% Forschung, 50% Lehre), ZHAW InIT
2012-2013: Gesamtverantwortung interne IT, TWT GmbH Science & Innovation
2011-2013: Fachgebietsleiter Smarte Software, TWT GmbH Science & Innovation
2010-2011: Software-Architekt & Projektleiter, TWT GmbH Science & Innovation
2004-2010: wiss. MA im Bereich Audio- und Videomining, Philipps-Universität Marburg
1998-2010: diverse Nebentätigkeiten in SW-Entwicklung und Data Mining

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
Amirian, Mohammadreza; Montoya-Zegarra, Javier A.; Herzig, Ivo; Eggenberger Hotz, Peter; Lichtensteiger, Lukas; Morf, Marco; Züst, Alexander; Paysan, Pascal; Peterlik, Igor; Scheib, Stefan; Füchslin, Rudolf Marcel; Stadelmann, Thilo; Schilling, Frank-Peter,

2023.

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks.

Medical Physics.

Verfügbar unter: https://doi.org/10.1002/mp.16405
Battaglia, Mattia; Comi, Ennio; Stadelmann, Thilo; Hiestand, Roman; Ruhstaller, Beat; Knapp, Evelyne,

2023.

Deep ensemble inverse model for image-based estimation of solar cell parameters.

APL Machine Learning.

Verfügbar unter: https://doi.org/10.21256/zhaw-28346
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), S. 222.

Verfügbar unter: https://doi.org/10.3390/jimaging8080222
Schilling, Frank-Peter; Flumini, Dandolo; Füchslin, Rudolf Marcel; Gavagnin, Elena; Geller, Armando; Quarteroni, Silvia; Stadelmann, Thilo,

2022.

Foundations of Data Science : a comprehensive overview formed at the 1st International Symposium on the Science of Data Science.

Archives of Data Science, Series A.

8(2).

Verfügbar unter: https://doi.org/10.5445/IR/1000146422
Stadelmann, Thilo; Klamt, Tino; Merkt, Philipp H.,

2022.

Data centrism and the core of Data Science as a scientific discipline.

Archives of Data Science, Series A.

8(2).

Verfügbar unter: https://doi.org/10.5445/IR/1000143637
Schmitt-Koopmann, Felix M.; Huang, Elaine M.; Hutter, Hans-Peter; Stadelmann, Thilo; Darvishy, Alireza,

2022.

FormulaNet : a benchmark dataset for mathematical formula detection.

IEEE Access.

10, S. 91588-91596.

Verfügbar unter: https://doi.org/10.1109/ACCESS.2022.3202639
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
Wehrli, Samuel; Hertweck, Corinna; Amirian, Mohammadreza; Glüge, Stefan; Stadelmann, Thilo,

2021.

Bias, awareness, and ignorance in deep-learning-based face recognition.

AI and Ethics.

2(3), S. 509-522.

Verfügbar unter: https://doi.org/10.1007/s43681-021-00108-6
Stadelmann, Thilo; Keuzenkamp, Julian; Grabner, Helmut; Würsch, Christoph,

2021.

The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid.

Education Sciences.

11(7), S. 318.

Verfügbar unter: https://doi.org/10.3390/educsci11070318
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
Dessimoz, Jean-Daniel; Koehler, Jana; Stadelmann, Thilo,

2015.

AI in Switzerland.

AI Magazine.

36(2), S. 102-105.

Verfügbar unter: https://doi.org/10.1609/aimag.v36i2.2591
Stockinger, Kurt; Stadelmann, Thilo,

2014.

Data Science für Lehre, Forschung und Praxis.

HMD Praxis der Wirtschaftsinformatik.

51(4), S. 469-479.

Verfügbar unter: https://doi.org/10.1365/s40702-014-0040-1

Bücher und Monographien, peer-reviewed

Stadelmann, Thilo; Schilling, Frank-Peter, Hrsg.,

2022.

Advances in deep neural networks for visual pattern recognition.

Basel:

MDPI.

Journal of Imaging ; 8.

Verfügbar unter: https://www.mdpi.com/journal/jimaging/special_issues/deep_neural_network
Schilling, Frank-Peter; Stadelmann, Thilo, Hrsg.,

2020.

Artificial neural networks in pattern recognition.

Basel:

MDPI.

Computers ; 9.

Verfügbar unter: https://www.mdpi.com/journal/computers/special_issues/ANNPR2020
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

2019.

Applied data science : lessons learned for the data-driven business.

1. Auflage.

Cham:

Springer.

ISBN 978-3-030-11820-4.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1

Buchbeiträge, peer-reviewed

Stadelmann, Thilo,

2019.

Wie maschinelles Lernen den Markt verändert

.

In:

Haupt, Reinhard; Schmitz, Stephan, Hrsg.,

Digitalisierung: Datenhype mit Werteverlust? : ethische Perspektiven für eine Schlüsseltechnologie.

Holzgerlingen:

SCM Hänssler.

S. 67-79.

Verfügbar unter: https://doi.org/10.21256/zhaw-18822
Stadelmann, Thilo; Tolkachev, Vasily; Sick, Beate; Stampfli, Jan; Dürr, Oliver,

2019.

Beyond ImageNet : deep learning in industrial practice

.

In:

Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

Applied data science : lessons learned for the data-driven business.

Cham:

Springer.

S. 205-232.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_12
Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt,

2019.

Data science

.

In:

Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

Applied data science : lessons learned for the data-driven business.

Cham:

Springer.

S. 17-29.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_2
Stadelmann, Thilo; Stockinger, Kurt; Heinatz-Bürki, Gundula; Braschler, Martin,

2019.

Data scientists

.

In:

Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

Applied data science : lessons learned for the data-driven business.

Cham:

Springer.

S. 31-45.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_3
Stadelmann, Thilo; Braschler, Martin; Stockinger, Kurt,

2019.

Introduction to applied data science

.

In:

Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

Applied data science : lessons learned for the data-driven business.

Cham:

Springer.

S. 3-16.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_1
Stockinger, Kurt; Braschler, Martin; Stadelmann, Thilo,

2019.

Lessons learned from challenging data science case studies

.

In:

Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

Applied data science : lessons learned for the data-driven business.

Cham:

Springer.

S. 447-465.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_24
Meierhofer, Jürg; Stadelmann, Thilo; Cieliebak, Mark,

2019.

Data products

.

In:

Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

Applied data science : lessons learned for the data-driven business.

Cham:

Springer.

S. 47-61.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_4
Hollenstein, Lukas; Lichtensteiger, Lukas; Stadelmann, Thilo; Amirian, Mohammadreza; Budde, Lukas; Meierhofer, Jürg; Füchslin, Rudolf Marcel; Friedli, Thomas,

2019.

Unsupervised learning and simulation for complexity management in business operations

.

In:

Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, Hrsg.,

Applied data science : lessons learned for the data-driven business.

Cham:

Springer.

S. 313-331.

Verfügbar unter: https://doi.org/10.1007/978-3-030-11821-1_17
Stockinger, Kurt; Stadelmann, Thilo; Ruckstuhl, Andreas,

2016.

Data Scientist als Beruf

.

In:

Fasel, Daniel; Andreas, Meier, Hrsg.,

Big Data.

Wiesbaden:

Springer.

S. 59-81.

Edition HMD.

Verfügbar unter: https://doi.org/10.1007/978-3-658-11589-0_4

Konferenzbeiträge, peer-reviewed

Luley, Paul-Philipp; Deriu, Jan Milan; Yan, Peng; Schatte, Gerrit A.; Stadelmann, Thilo,

2023.

From concept to implementation : the data-centric development process for AI in industry [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. 73-76.

Verfügbar unter: https://doi.org/10.1109/SDS57534.2023.00017
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
Ali, Waqar; Vascon, Sebastiano; Stadelmann, Thilo; Pelillo, Marcello,

2023.

Quasi-CliquePool : hierarchical graph pooling for graph classification [Paper].

In:

SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing.

2nd Graph Models for Learning and Recognition (GMLR 2023) Track at the 38th ACM/SIGAPP Symposium on Applied Computing (SAC 2023), Tallinn, Estonia, 27 March - 2 April 2023.

New York:

Association for Computing Machinery.

S. 544-552.

Verfügbar unter: https://doi.org/10.1145/3555776.3578600
Herzig, Ivo; Paysan, Pascal; Scheib, Stefan; Züst, Alexander; Schilling, Frank-Peter; Montoya, Javier; Amirian, Mohammadreza; Stadelmann, Thilo; Eggenberger Hotz, Peter; Füchslin, Rudolf Marcel; Lichtensteiger, Lukas,

2022.

Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT [Poster].

In:

AAPM Annual Meeting, Washington, DC, USA, 10-14 July 2022.

American Association of Physicists in Medicine.

S. e325-e326.

Verfügbar unter: https://doi.org/10.1002/mp.15769
Amirian, Mohammadreza; Montoya, Javier; Gruss, Jonathan; Stebler, Yves D.; Bozkir, Ahmet Selman; Calandri, Marco; Schwenker, Friedhelm; Stadelmann, Thilo,

2021.

PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis [Paper].

In:

Proceedings of CISP-BMEI’21.

14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China, 23-25 October 2021.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-23318
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.

S. 26-31.

Verfügbar unter: https://doi.org/10.1109/SDS51136.2021.00012
Knapp, Evelyne; Battaglia, Mattia; Stadelmann, Thilo; Jenatsch, Sandra; Ruhstaller, Beat,

2021.

XGBoost trained on synthetic data to extract material parameters of organic semiconductors [Paper].

In:

Proceedings of the 8th SDS.

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

IEEE.

S. 46-51.

Verfügbar unter: https://doi.org/10.1109/SDS51136.2021.00015
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
Glüge, Stefan; Amirian, Mohammadreza; Flumini, Dandolo; Stadelmann, Thilo,

2020.

How (not) to measure bias in face recognition networks [Paper].

In:

Schilling, Frank-Peter; Stadelmann, Thilo, Hrsg.,

Artificial Neural Networks in Pattern Recognition.

9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.

Cham:

Springer.

Lecture Notes in Computer Science ; 12294.

Verfügbar unter: https://doi.org/10.1007/978-3-030-58309-5_10
Schilling, Frank-Peter; Stadelmann, Thilo, Hrsg.,

2020.

Artificial neural networks in pattern recognition : proceedings of the 9th IAPR TC3 workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020.

9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'20), Winterthur, Switzerland, 2-4 September 2020.

Springer.

Lecture Notes in Computer Science ; 12294.

ISBN 978-3-030-58308-8.

Verfügbar unter: https://doi.org/10.1007/978-3-030-58309-5
Roost, Dano; Meier, Ralph; Toffetti Carughi, Giovanni; Stadelmann, Thilo,

2020.

Combining reinforcement learning with supervised deep learning for neural active scene understanding [Paper].

In:

Active Vision and Perception in Human(-Robot) Collaboration Workshop at IEEE RO-MAN 2020 (AVHRC’20), online, 31 August - 4 September 2020.

University of Essex.

Verfügbar unter: https://doi.org/10.21256/zhaw-20419
Roost, Dano; Meier, Ralph; Huschauer, Stephan; Nygren, Erik; Egli, Adrian; Weiler, Andreas; Stadelmann, Thilo,

2020.

Improving sample efficiency and multi-agent communication in RL-based train rescheduling [Paper].

In:

Proceedings of the 7th SDS.

7th Swiss Conference on Data Science, Lucerne, Switzerland, 26 June 2020.

IEEE.

Verfügbar unter: https://doi.org/10.21256/zhaw-19978
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; Glinski-Haefeli, Sebastian; Gerber, Patrick; Dürr, Oliver,

2018.

Capturing suprasegmental features of a voice with RNNs for improved speaker clustering [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. 333-345.

Lecture Notes in Computer Science ; 11081.

Verfügbar unter: https://doi.org/10.1007/978-3-319-99978-4_26
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
Meier, Benjamin Bruno; Elezi, Ismail; Amirian, Mohammadreza; Dürr, Oliver; Stadelmann, Thilo,

2018.

Learning neural models for end-to-end clustering [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. 126-138.

Lecture Notes in Computer Science ; 11081.

Verfügbar unter: https://doi.org/10.1007/978-3-319-99978-4_10
Hibraj, Feliks; Vascon, Sebastiano; Stadelmann, Thilo; Pelillo, Marcello,

2018.

Speaker clustering using dominant sets [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. 3549-3554.

Verfügbar unter: https://doi.org/10.1109/ICPR.2018.8546067
Amirian, Mohammadreza; Schwenker, Friedhelm; Stadelmann, Thilo,

2018.

Trace and detect adversarial attacks on CNNs using feature response maps [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. 346-358.

Lecture Notes in Computer Science ; 11081.

Verfügbar unter: https://doi.org/10.1007/978-3-319-99978-4_27
Meier, Benjamin; Stadelmann, Thilo; Stampfli, Jan; Arnold, Marek; Cieliebak, Mark,

2017.

Fully convolutional neural networks for newspaper article segmentation [Paper].

In:

Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

14th IAPR International Conference on Document Analysis and Recognition (ICDAR 2017), Kyoto Japan, 13-15 November 2017.

Kyoto:

CPS.

Verfügbar unter: https://doi.org/10.21256/zhaw-1533
Lukic, Yanick X.; Vogt, Carlo; Dürr, Oliver; Stadelmann, Thilo,

2017.

Learning embeddings for speaker clustering based on voice equality [Paper].

In:

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP).

27th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2017), Tokyo, 25-28 September 2017.

IEEE.

Verfügbar unter: https://doi.org/10.1109/MLSP.2017.8168166
Lukic, Yanick; Vogt, Carlo; Dürr, Oliver; Stadelmann, Thilo,

2016.

Speaker identification and clustering using convolutional neural networks [Paper].

In:

2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),.

26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2016), Vietri sul Mare, Italy, 13-16 Sept. 2016.

IEEE.

Verfügbar unter: https://doi.org/10.1109/MLSP.2016.7738816
Arnold, Marek; Cieliebak, Mark; Stadelmann, Thilo; Stampfli, Jan; Uzdilli, Fatih,

2015.

PANOPTES : automated article segmentation of newspaper pages for "Real Time Print Media Monitoring“ [Poster].

In:

Proceedings of SGAICO Annual Assembly and Workshop 2015.

SGAICO Annual Assembly and Workshop 2015, Geneva, 12 November 2015.

Verfügbar unter: https://sgaico.swissinformatics.org/wp-content/uploads/sites/34/2017/04/2015_SGAICOPoster-TSD.pdf
Stadelmann, Thilo; Stockinger, Kurt; Braschler, Martin; Cieliebak, Mark; Baudinot, Gerold; Dürr, Oliver; Ruckstuhl, Andreas,

2013.

Applied data science in Europe : challenges for academia in keeping up with a highly demanded topic [Paper].

In:

Proceedings of the 9th European Computer Science Summit.

9th European Computer Science Summit, Amsterdam, Niederlande, 8-9 October 2013.

Weitere Publikationen

Segessenman, Jan; Stadelmann, Thilo; Andrew, Davison; Oliver, Dürr,

2023.

Assessing deep learning : a work program for the humanities in the age of artificial intelligence.

SSRN.

Verfügbar unter: https://doi.org/10.21256/zhaw-28651
von der Malsburg, Christoph; Stadelmann, Thilo; Grewe, Benjamin F.,

2022.

A theory of natural intelligence.

arXiv.

Verfügbar unter: https://doi.org/10.48550/ARXIV.2205.00002
Stadelmann, Thilo; Würsch, Christoph,

2020.

Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept.

Winterthur:

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-20885
Stadelmann, Thilo; Schilling, Frank-Peter,

2019.

Deep Learning in medizinischer Diagnostik und Qualitätskontrolle.

Netzwoche.

Verfügbar unter: https://doi.org/10.21256/zhaw-20163
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
Stadelmann, Thilo; Cieliebak, Mark; Stockinger, Kurt,

2015.

Toward automatic data curation for open data.

ERCIM News.

2015(100), S. 32-33.

Verfügbar unter: https://doi.org/10.21256/zhaw-3643

Mündliche Konferenzbeiträge und Abstracts

Stadelmann, Thilo,

2023.

KI als Chance für die angewandten Wissenschaften im Wettbewerb der Hochschulen.

In:

Bürgenstock-Konferenz der Schweizer Fachhochschulen und Pädagogischen Hochschulen, Luzern, Schweiz, 20.-21. Januar 2023.

Verfügbar unter: https://www.buergenstock-konferenz.ch/images/2023/19_Website_Eingabe_Stadelmann.pdf
von der Malsburg, Christoph; Grewe, Benjamin F.; Stadelmann, Thilo,

2022.

Making sense of the natural environment.

In:

The Biannual Conference of the German Cognitive Science Society (KogWis), Freiburg, Germany, 5-7 September 2022.

Verfügbar unter: https://stdm.github.io/downloads/papers/KogWis_2022.pdf

Publikationen vor Tätigkeit an der ZHAW

Thilo Stadelmann, Sven Johr, Michael Ditze, Florian Dittman, and Viktor Fässler. "FABELHAFT - Fahrerablenkung: Entwicklung eines Meta-Fahrerassistenzsystems durch Echtzeit-Audioklassifikation". In Proceedings of 28. VDI-VW Gemeinschaftstagung Fahrerassistenzsysteme und Integrierte Sicherheit, Wolfsburg, Germany, October 10.-11., 2012. VDI Wissensforum. URL stdm.github.io/downloads/papers/VDIFASIS_2012.pdf.

Thilo Stadelmann. "Voice Modeling Methods for Automatic Speaker Recognition". Dissertation, Philipps-Universität Marburg. Available online, 2010. URL stdm.github.io/downloads/papers/PhdThesis_2010.pdf.

Christian Beecks, Thilo Stadelmann, Bernd Freisleben, and Thomas Seidl. "Visual Speaker Model Exploration", In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME'2010), pages 727-728, Singapore, July 19-23, 2010, IEEE. URL stdm.github.io/downloads/papers/ICME_2010.pdf.

Thilo Stadelmann, Yinghui Wang, Matthew Smith, Ralph Ewerth, and Bernd Freisleben. "Rethinking Algorithm Development and Design in Speech Processing". In Proceedings of the 20th International Conference on Pattern Recognition (ICPR'10), pages 4476-4479, Istanbul, Turkey, August 2010a. IAPR. URL stdm.github.io/downloads/papers/ICPR_2010b.pdf.

Thilo Stadelmann and Bernd Freisleben. "On the MixMax Model and Cepstral Features for Noise-Robust Voice Recognition". Technical Report, Marburg University, July 2010. URL stdm.github.io/downloads/papers/TR_2010.pdf.

Thilo Stadelmann and Bernd Freisleben. Dimension-Decoupled Gaussian Mixture Model for Short Utterance Speaker Recognition. In Proceedings of the 20th International Conference on Pattern Recognition (ICPR'10), pages 1602-1605, Istanbul, Turkey, August 2010a. IAPR. URL stdm.github.io/downloads/papers/ICPR_2010a.pdf.

Markus Mühling, Ralph Ewerth, Thilo Stadelmann, Bing Shi, and Bernd Freisleben. "University of Marburg at TRECVID 2009: High-Level Feature Extraction". In Proceedings of TREC Video Retrieval Evaluation Workshop (TRECVid'09). Available online, 2009. URL stdm.github.io/downloads/papers/TRECVID_2009.pdf.

Ernst Juhnke, Dominik Seiler, Thilo Stadelmann, Tim Dörnemann, and Bernd Freisleben. "LCDL: An Extensible Framework for Wrapping Legacy Code". In Proceedings of International Workshop on @WAS Emerging Research Projects, Applications and Services (ERPAS'09), pages 638-642, Kuala Lumpur, Malaysia, December 2009. URL stdm.github.io/downloads/papers/ERPAS_2009.pdf.

Dominik Seiler, Ralph Ewerth, Steffen Heinzl, Thilo Stadelmann, Markus Mühling, Bernd Freisleben, and Manfred Grauer. "Eine Service-Orientierte Grid-Infrastruktur zur Unterstützung Medienwissenschaftlicher Filmanalyse". In Proceedings of the Workshop on Gemeinschaften in Neuen Medien (GeNeMe'09), pages 79-89, Dresden, Germany, September 2009. URL stdm.github.io/downloads/papers/GeNeMe_2009.pdf.

Thilo Stadelmann and Bernd Freisleben. "Unfolding Speaker Clustering Potential: A Biomimetic Approach". In Proceedings of the ACM International Conference on Multimedia (ACMMM'09), pages 185-194, Beijing, China, October 2009. ACM. URL stdm.github.io/downloads/papers/ACMMM_2009.pdf.

Thilo Stadelmann, Steffen Heinzl, Markus Unterberger, and Bernd Freisleben. "WebVoice: A Toolkit for Perceptual Insights into Speech Processing". In Proceedingsof the 2nd International Congress on Image and Signal Processing (CISP'09), pages 4358-4362, Tianjin, China, October 2009. URL stdm.github.io/downloads/papers/CISP_2009.pdf.

Steffen Heinzl, Markus Mathes, Thilo Stadelmann, Dominik Seiler, Marcel Diegelmann, Helmut Dohmann, and Bernd Freisleben. "The Web Service Browser: Automatic Client Generation and Efficient Data Transfer for Web Services". In Proceedings of the 7th IEEE International Conference on Web Services (ICWS'09), pages 743-750, Los Angeles, CA, USA, July 2009a. IEEE Press. URL stdm.github.io/downloads/papers/ICWS_2009.pdf.

Steffen Heinzl, Dominik Seiler, Ernst Juhnke, Thilo Stadelmann, Ralph Ewerth, Manfred Grauer, and Bernd Freisleben. "A Scalable Service-Oriented Architecture for Multimedia Analysis, Synthesis, and Consumption". International Journal of Web and Grid Services, 5(3):219-260, 2009b. Inderscience Publishers. URL stdm.github.io/downloads/papers/IJWGS_2009.pdf.

Markus Mühling, Ralph Ewerth, Thilo Stadelmann, Bing Shi, and Bernd Freisleben. "University of Marburg at TRECVID 2008: High-Level Feature Extraction". In Proceedings of TREC Video Retrieval Evaluation Workshop (TRECVid'08). Available online, 2008. URL stdm.github.io/downloads/papers/TRECVID_2008.pdf.

Markus Mühling, Ralph Ewerth, Thilo Stadelmann, Bing Shi, Christian Zöfel, and Bernd Freisleben. "University of Marburg at TRECVID 2007: Shot Boundary Detection and High-Level Feature Extraction". In Proceedings of TREC Video Retrieval Evaluation Workshop (TRECVid'07). Available online, 2007a. URL stdm.github.io/downloads/papers/TRECVID_2007.pdf.

Ralph Ewerth, Markus Mühling, Thilo Stadelmann, Julinda Gllavata, Manfred Grauer, and Bernd Freisleben. "Videana: A Software Toolkit for Scientific Film Studies". In Proceedings of the International Workshop on Digital Tools in Film Studies, pages 1-16, Siegen, Germany, 2007. Transcript Verlag. URL stdm.github.io/downloads/papers/DTiMS_2007.pdf.

Markus Mühling, Ralph Ewerth, Thilo Stadelmann, Bernd Freisleben, Rene Weber, and Klaus Mathiak. "Semantic Video Analysis for Psychological Research on Violence in Computer Games". In Proceedings of the ACM International Conference on Image and Video Retrieval (CIVR'07), pages 611-618, Amsterdam, The Netherlands, July 2007b. ACM. URL stdm.github.io/downloads/papers/CIVR_2007.pdf.

Ralph Ewerth, Markus Mühling, Thilo Stadelmann, Ermir Qeli, Björn Agel, Dominik Seiler, and Bernd Freisleben. "University of Marburg at TRECVID 2006: Shot Boundary Detection and Rushes Task Results". In Proceedings of TREC Video Retrieval Evaluation Workshop (TRECVid'06). Available online, 2006. URL stdm.github.io/downloads/papers/TRECVID_2006.pdf.

Thilo Stadelmann and Bernd Freisleben. "Fast and Robust Speaker Clustering Using the Earth Mover's Distance and MixMax Models". In Proceedings of the 31st IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06), volume 1, pages 989-992, Toulouse, France, April 2006. IEEE. URL stdm.github.io/downloads/papers/ICASSP_2006.pdf.

Ralph Ewerth, Christian Behringer, Tobias Kopp, Michael Niebergall, Thilo Stadelmann, and Bernd Freisleben. "University of Marburg at TRECVID 2005: Shot Boundary Detection and Camera Motion Estimation Results". In Proceedings of TREC Video Retrieval Evaluation Workshop (TRECVid'05). Available online, 2005. URL stdm.github.io/downloads/papers/TRECVID_2005.pdf.

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