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

Arbeit an der ZHAW

Tätigkeit

Arbeits- und Forschungsschwerpunkte

Künstliche Intelligenz und maschinelles Lernen: Deep Learning, Representation Learning, Mustererkennung, Computer Vision, Audioanalyse, intelligente Systeme
Forschungsinteressen: Robustes und praktisches Deep Learning, industrielle Bildverarbeitung, medizinische Bildverarbeitung, Weltmodelle, generellere Künstliche Intelligenz, vertrauenswürdige KI, Lernen zu lernen, Dokumentenerkennung, Stimmerkennung, Datenwissenschaft
Data Science

Lehrtätigkeit

Lehrtätigkeit in der Weiterbildung

CAS Information Engineering

Berufserfahrung

Gründer, Verwaltungsrat
AlpineAI AG
07 / 2023 - heute
Keynote Redner
Premium Speakers Agency
05 / 2023 - heute
Professor für AI/ML, Leiter ZHAW CAI, Leiter MPC Forschungsgruppe
ZHAW School of Engineering
04 / 2021 - heute
Co-Gründer und Vorstand
ZHAW Datalab
05 / 2013 - heute
Vorstand
Data innovation alliance
01 / 2016 - 11 / 2023
Wissenschaftlicher Leiter
ZHAW digital
01 / 2019 - 03 / 2021
Professor für Informatik
ZHAW Institut für angewandte Informationstechnologie
07 / 2018 - 03 / 2021
Stv. Leitung Forschungsschwerpunkt Information Engineering
ZHAW Institut für angewandte Informationstechnologie
06 / 2015 - 03 / 2021
Leiter
ZHAW Datalab
05 / 2013 - 12 / 2018
Vizepräsident
Swiss Group of Artificial Intelligence and Cognitive Science (SGAICO)
02 / 2014 - 09 / 2018
Dozent für Information Engineering
ZHAW Institut für angewandte Informationstechnologie
02 / 2013 - 06 / 2018
Managing Director ad interim
Swiss Alliance for Data-Intensive Services
08 / 2017 - 02 / 2018
Co-Organisator
Zürich Machine Learning & Data Science Meetup
09 / 2015 - 03 / 2017
Fachgebietsleiter Smarte Software
TWT GmbH Science & Innovation
04 / 2011 - 06 / 2013
Gesamtverantwortung interne IT
TWT GmbH Science & Innovation
04 / 2012 - 01 / 2013
Software-Architekt & Projektleiter
TWT GmbH Science & Innovation
07 / 2010 - 03 / 2011
Wissenschaftlicher Mitarbeiter im Bereich Audio- und Videomining
Philipps-Universität Marburg
09 / 2004 - 06 / 2010
Diverse Nebentätigkeiten in SW-Entwicklung und Data Mining
diverse
02 / 1998 - 06 / 2010

Aus- und Weiterbildung

Ausbildung

Dr. rer. nat. / Informatik
Philipps-Universität Marburg
08 / 2004 - 07 / 2010
Dipl. Inform. (FH) / Technisch-wissenschaftliche Informatik
Fachhochschule Giessen-Friedberg
09 / 2000 - 07 / 2004

Weiterbildung

Führungsausbildung ZHAW
Zürcher Hochschule für Angewandte Wissenschaften
06 / 2018
CAS Hochschuldidaktik
Pädagogische Hochschule Zürich
06 / 2015

Netzwerk

Mitglied in Netzwerken

ORCID digital identifier

ORCID ID: 0000-0002-3784-0420

Auszeichnungen

Finalist - "The Pascal"
Digital Economy Award
09 / 2024
Honorable Mention - Best Paper Award
IEEE Swiss Conference on Data Science 2024
06 / 2024
Betreuer mehrerer prämierter Abschlussarbeiten (u.a. ZHAW SGD Award, Siemens Excellence Award, Lab Sciences Award, Dr. Waldemar Jucker Award)
verschiedene
01 / 2024
Most Cited Paper Award für "Automated Machine Learning in Practice"
IEEE SDS 2023
06 / 2023
Impact Award 2022
ZHAW digital
12 / 2022
DIZH Fellowship 2022
ZHAW digital
11 / 2022
Swiss Data Science Conference Best Paper Award 2021
SDS 2021
06 / 2021
Swiss Data Science Conference Best Poster Presentation Award 2020
SDS 2020
06 / 2020
Lehrpreis "Best Teaching - Best Practices"
ZHAW
09 / 2019

Social Media

Medienpräsenz

Projekte

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

Ali, Waqar; Vascon, Sebastiano; Stadelmann, Thilo; Pelillo, Marcello,

2024.

Hierarchical glocal attention pooling for graph classification.

Pattern Recognition Letters.

186, S. 71-77.

Verfügbar unter: https://doi.org/10.1016/j.patrec.2024.09.009
Jermain, Peter R; Oswald, Martin; Langdun, Tenzin; Wright, Santana; Khan, Ashraf; Stadelmann, Thilo; Abdulkadir, Ahmed; Yaroslavsky, Anna N.,

2024.

Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer.

Scientific Reports.

14(1), S. 16389.

Verfügbar unter: https://doi.org/10.1038/s41598-024-64855-2
Dashti, Ali; Stadelmann, Thilo; Kohl, Thomas,

2024.

Machine learning for robust structural uncertainty quantification in fractured reservoirs.

Geothermics.

120(103012).

Verfügbar unter: https://doi.org/10.1016/j.geothermics.2024.103012
Schmitt-Koopmann, Felix; Huang, Elaine M.; Hutter, Hans-Peter; Stadelmann, Thilo; Darvishy, Alireza,

2024.

MathNet : a data-centric approach for printed mathematical expression recognition.

IEEE Access.

12, S. 76963-76974.

Verfügbar unter: https://doi.org/10.1109/ACCESS.2024.3404834
Neururer, Daniel; Dellwo, Volker; Stadelmann, Thilo,

2024.

Deep neural networks for automatic speaker recognition do not learn supra-segmental temporal features.

Pattern Recognition Letters.

181, S. 64-69.

Verfügbar unter: https://doi.org/10.1016/j.patrec.2024.03.016
Yan, Peng; Abdulkadir, Ahmed; Luley, Paul-Philipp; Rosenthal, Matthias; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo,

2024.

A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions.

IEEE Access.

12, S. 3768-3789.

Verfügbar unter: https://doi.org/10.1109/ACCESS.2023.3349132
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,

2024.

Real world music object recognition.

Transactions of the International Society for Music Information Retrieval.

7(1), S. 1-14.

Verfügbar unter: https://doi.org/10.5334/tismir.157
Segessenmann, Jan; Stadelmann, Thilo; Davison, Andrew; Dürr, Oliver,

2023.

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

AI and Ethics.

Verfügbar unter: https://doi.org/10.1007/s43681-023-00408-z
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.

50(10), S. 6228-6242.

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.

1(3), S. 036108.

Verfügbar unter: https://doi.org/10.1063/5.0139707
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,

2024.

Wegweiser Künstliche Intelligenz : verstehen, anwenden und zuversichtlich Zukunft gestalten

.

In:

Hersberger, Sebastian; Hoffmann, Christian Hugo, Hrsg.,

Wie die Künstliche Intelligenz die Wirtschaft verändert : Überblick über die theoretischen Grundlagen und Praxisbeispiele entlang von Ökosystemen.

Cham:

Springer.
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

Bolt, Peter; Ziebart, Volker; Jaeger, Christian; Schmid, Nicolas; Stadelmann, Thilo; Füchslin, Rudolf Marcel,

2024.

A simulation study on energy optimization in building control with reinforcement learning [Paper].

In:

11th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'24), Montreal, Canada, 10-12 October 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-31129
Yan, Peng; Abdulkadir, Ahmed; Aguzzi, Giulia; Schatte, Gerrit A.; Grewe, Benjamin F.; Stadelmann, Thilo,

2024.

Automated process monitoring in injection molding via representation learning and setpoint regression [Paper].

In:

2024 11th IEEE Swiss Conference on Data Science (SDS).

11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.

IEEE.

S. 138-145.

Verfügbar unter: https://doi.org/10.1109/SDS60720.2024.00027
Meyer, Benjamin; Stadelmann, Thilo; Lüthi, Marcel,

2024.

ScalaGrad : a statically typed automatic differentiation library for safer data science [Paper].

In:

2024 11th IEEE Swiss Conference on Data Science (SDS).

11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.

IEEE.

S. 229-232.

Verfügbar unter: https://doi.org/10.1109/SDS60720.2024.00040
Tuggener, Lukas; Sager, Pascal; Taoudi-Benchekroun, Yassine; Grewe, Benjamin F.; Stadelmann, Thilo,

2024.

So you want your private LLM at home? : a survey and benchmark of methods for efficient GPTs [Paper].

In:

2024 11th IEEE Swiss Conference on Data Science (SDS).

11th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 30-31 May 2024.

IEEE.

Verfügbar unter: https://doi.org/10.1109/SDS60720.2024.00036
Lanfant, Briac; Lack, Silvan; Meyer, Benjamin; Abdulkadir, Ahmed; Stadelmann, Thilo; Schmid, Daniel,

2024.

3D-master-based method for optimizing the cost calculation of PBF-LB/M manufactured parts [Paper].

In:

Metal Additive Manufacturing Conference (MAMC), Aachen, Germany, 17-19 September 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Begga, Ahmed; Ali, Waqar; Niculescu, Gabriel; Escolano, Francisco; Stadelmann, Thilo; Pelillo, Marcello,

2024.

Community-hop : enhancing node classification through community preference [Paper].

In:

Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition and Structural and Syntactic Pattern Recognition (S+SSPR), Venice, Italy, 9-10 September 2024.

ZHAW Zürcher Hochschule für Angewandte Wissenschaften.

Verfügbar unter: https://doi.org/10.21256/zhaw-31438
Jermain, Peter R.; Oswald, Martin; Langdun, Tenzin; Wright, Santana; Khan, Ashraf; Stadelmann, Thilo; Abdulkadir, Ahmed; Yaroslavsky, Ann N.,

2024.

Rapid optical cytology with deep learning-based cell segmentation for diagnosis of thyroid lesions [Paper].

In:

Optica Biophotonics Congress: Biomedical Optics 2024 (Translational, Microscopy, OCT, OTS, BRAIN).

Optica Biophotonics Congress: Biomedical Optics, Fort Lauderdale, USA, 7-10 April 2024.

Optica Publishing Group.

S. MTu4A.5.

Verfügbar unter: https://doi.org/10.1364/MICROSCOPY.2024.MTu4A.5
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].

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2019.

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

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2018.

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

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Stadelmann, Thilo; Glinski-Haefeli, Sebastian; Gerber, Patrick; Dürr, Oliver,

2018.

Capturing suprasegmental features of a voice with RNNs for improved speaker clustering [Paper].

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2018.

Deep watershed detector for music object recognition [Paper].

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

2018.

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

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2018.

Learning neural models for end-to-end clustering [Paper].

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Hibraj, Feliks; Vascon, Sebastiano; Stadelmann, Thilo; Pelillo, Marcello,

2018.

Speaker clustering using dominant sets [Paper].

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Amirian, Mohammadreza; Schwenker, Friedhelm; Stadelmann, Thilo,

2018.

Trace and detect adversarial attacks on CNNs using feature response maps [Paper].

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Meier, Benjamin; Stadelmann, Thilo; Stampfli, Jan; Arnold, Marek; Cieliebak, Mark,

2017.

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

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Lukic, Yanick X.; Vogt, Carlo; Dürr, Oliver; Stadelmann, Thilo,

2017.

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

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2016.

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

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

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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].

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Proceedings of SGAICO Annual Assembly and Workshop 2015.

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Swiss Group for Artificial Intelligence and Cognitive Science.

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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].

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