Prof. Dr. Thilo Stadelmann

Personal profile

Management role

• Head of Centre, Centre for Artificial Intelligence
• Head, Computer Vision, Perception and Cognition Group

Position at the ZHAW

Scientist, lecturer, speaker, consultant, networker, principal investigator, reviewer, mentor, leader, father, believer.

Roles:

Director of Centre, CAI: www.zhaw.ch/en/engineering/institutes-centres/cai/
Professor for AI/ML (Chair of Engineering - Information): stdm.github.io/research/
Head of Computer Vision, Perception and Cognition Group: www.zhaw.ch/en/engineering/institutes-centres/cai/computer-vision-perception-and-cognition-group/
Keynote speaker, Premium Speakers: premium-speakers.com/en/speaker-presenter/thilo-stadelmann/

Activities:

Talks and scientific community engagement: stdm.github.io/service/
ZHAW staff prayer meeting: stdm.github.io/prayer

Awards:

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 teaching award "Best Teaching - Best Practices" (3rd rank) 2019

stdm.github.io

Professional development teaching

• CAS Information Engineering
• CAS Machine Intelligence

Expertise and research interests

Artificial intelligence and machine learning: deep learning, pattern recognition, computer vision, audio analysis, intelligent sysetems.

Current research interests: robust and efficient deep learning in neural networks for practical use cases, reinforcement learning for autonomous systems, more general and biologically inspired AI, large language models; ethical and societal aspects of such systems.

See stdm.github.io/research/

Educational background

Executive leadership training ZHAW, 2018, Zurich University of Applied Sciences
Certificate of Advanced Studies in didactics of higher education, 2015, Zurich University of Teacher Education
PhD studies, Dr. rer. nat (PhD equivalent), 2010, University of Marburg
Computer science studies, Dipl. Inform. (FH) (MSc equivalent), 2004, Giessen University of Applied Sciences

Professional milestones

2023-present: Founder and member of the board, AlpineAI AG
2023-present: Keynote speaker, Premium Speakers Agency
2021-present: Professor of AI/ML, Director of ZHAW CAI, Head of CVPC Group
2019-2021: Scientific Director, ZHAW digital
2018-2021: Professor of Computer Science, ZHAW InIT
2017-2018: Managing Director ad Interim, Swiss Alliance for Data-Intensive Services
2016-present: Board of the data innovation alliance
2015-2021: Deputy head of Information Engineering research group, ZHAW
2015-2017: Co-organizer Zurich Machine Learning & Data Science Meetup
2014-2018: Vice president of SGAICO, Swiss Group of Artificial Intelligence and Cognitive Science
2013-2019: Head of the board, ZHAW Datalab
2013-present: Co-Founder & member of the board, ZHAW Datalab
2013-2018: Lecturer Information Engineering (50% research, 50% teaching), ZHAW InIT
2012-2013: Director of internal IT, TWT GmbH Science & Innovation
2011-2013: Head of smart software team, TWT GmbH Science & Innovation
2010-2011: Software architect and project leader, TWT GmbH Science & Innovation
2004-2010: Research assistant in the area of audio- and video mining, University of Marburg
1998-2010: several sideline jobs in software development and data mining

Membership of networks

• Datalab, the ZHAW Data Science Laboratory (member of the board)
• Premium Speakers Agency
• data innovation alliance (member of the board)
• European Centre for Living Technology ECLT (fellow)
• IAPR TC3, the Technical Committee on Neural Networks & Computational Intelligence of the International Association for Pattern Recognition
• Deutsche Arbeitsgemeinschaft für Mustererkennung e.V. DAGM (German Pattern Recognition Association)
• Gesellschaft für Klassifikation - Data Science Society
• IEEE CS, CIS, SMC, SP (senior member)
• CLAIRE, the Confederation of Laboratories for Artificial Intelligence Research in Europe
• LinkedIn

Projects

• Stability of self-organizing net fragments as inductive bias for next-generation deep learning / Project leader / Project ongoing
• ML-BCA: Machine Learning for Body Composition Analysis / Project leader / Project ongoing
• Master3D – 3D-Master for a Digitized Manufacturing Platform / Deputy project leader / Project ongoing
• certAInty – A Certification Scheme for AI systems / Team member / Project ongoing
• DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning / Project leader / Project ongoing
• LINA: Shared Large-scale Infrastructure for the Development and Safe Testing of Autonomous Systems / Project leader / Project ongoing
• AC3T – AI powered CBCT for improved Combination Cancer Therapy / Team member / Project ongoing
• Accessible Scientific PDFs for All / Project co-leader / Project ongoing
• Mobile Inclusion Lab / Project co-leader / Project completed
• AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support / Project co-leader / Project completed
• TAILOR – Trustworthy and sample efficient vision transformers / Project co-leader / Project completed
• Pilot study machine learning for injection molding processes / Project leader / Project completed
• DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes / Team member / Project completed
• TAILOR – Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization / Project leader / Project completed
• RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Project co-leader / Project completed
• FWA: Visual Food Waste Analysis for Sustainable Kitchens / Project co-leader / Project completed
• Feasibility Study Reinforcement Learning for Heating Systems / Team member / Project completed
• Radiosands / Team member / Project completed
• Ada – Advanced Algorithms for an Artificial Data Analyst / Project leader / Project completed
• QualitAI - Quality control of industrial products via deep learning on images / Project leader / Project completed
• FarmAI – Artificial intelligence for Farming Simulator / Team member / Project completed
• Libra: A One-Tool Solution for MLD4 Compliance / Team member / Project completed
• DeepText: Intelligent Text Analysis with Deep Learning / Team member / Project completed
• DeLLA: Deep-Learning-based speech recognition with limited training material / Team member / Project completed
• DeepScore: Digital Music Stand with Musical Understanding via Active Sheet Technology / Project leader / Project completed
• Complexity 4.0 / Deputy project leader / Project completed
• PANOPTES / Team member / Project completed
• DaCoMo - Data-Driven Condition Monitoring / Project leader / Project completed
• iisiBox - Easy access to educational servers. / Project leader / Project completed
• MobileMall / Project leader / Project completed
• SODES: Swiss Open Data Exploration System / Team member / Project completed
• Talkalyzer / 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

• 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.

  Available from: 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.

  Available from: 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), pp. 222.

  Available from: 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).

  Available from: 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).

  Available from: 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, pp. 91588-91596.

  Available from: 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).

  Available from: 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), pp. 509-522.

  Available from: 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), pp. 318.

  Available from: 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), pp. 510-538.

  Available from: https://doi.org/10.3390/ai1040031

• Dessimoz, Jean-Daniel; Koehler, Jana; Stadelmann, Thilo,

  2015.

  AI in Switzerland.

  AI Magazine.

  36(2), pp. 102-105.

  Available from: 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), pp. 469-479.

  Available from: https://doi.org/10.1365/s40702-014-0040-1

• Stadelmann, Thilo; Schilling, Frank-Peter, eds.,

  2022.

  Advances in deep neural networks for visual pattern recognition.

  Basel:

  MDPI.

  Journal of Imaging ; 8.

  Available from: https://www.mdpi.com/journal/jimaging/special_issues/deep_neural_network

• Schilling, Frank-Peter; Stadelmann, Thilo, eds.,

  2020.

  Artificial neural networks in pattern recognition.

  Basel:

  MDPI.

  Computers ; 9.

  Available from: https://www.mdpi.com/journal/computers/special_issues/ANNPR2020

• Braschler, Martin; Stadelmann, Thilo; Stockinger, Kurt, eds.,

  2019.

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

  1. Auflage.

  Cham:

  Springer.

  ISBN 978-3-030-11820-4.

  Available from: https://doi.org/10.1007/978-3-030-11821-1

• Stadelmann, Thilo,

  2019.

  Wie maschinelles Lernen den Markt verändert

  .

  In:

  Haupt, Reinhard; Schmitz, Stephan, eds.,

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

  Holzgerlingen:

  SCM Hänssler.

  pp. 67-79.

  Available from: 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, eds.,

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

  Cham:

  Springer.

  pp. 205-232.

  Available from: 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, eds.,

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

  Cham:

  Springer.

  pp. 17-29.

  Available from: 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, eds.,

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

  Cham:

  Springer.

  pp. 31-45.

  Available from: 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, eds.,

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

  Cham:

  Springer.

  pp. 3-16.

  Available from: 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, eds.,

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

  Cham:

  Springer.

  pp. 447-465.

  Available from: 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, eds.,

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

  Cham:

  Springer.

  pp. 47-61.

  Available from: 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, eds.,

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

  Cham:

  Springer.

  pp. 313-331.

  Available from: 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, eds.,

  Big Data.

  Wiesbaden:

  Springer.

  pp. 59-81.

  Edition HMD.

  Available from: https://doi.org/10.1007/978-3-658-11589-0_4

• 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.

  pp. 73-76.

  Available from: 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.

  pp. 85-88.

  Available from: 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.

  pp. 544-552.

  Available from: 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.

  pp. e325-e326.

  Available from: 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.

  Available from: 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.

  pp. 26-31.

  Available from: 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.

  pp. 46-51.

  Available from: 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.

  Available from: 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.

  pp. 9188-9195.

  Available from: 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, eds.,

  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.

  Available from: https://doi.org/10.1007/978-3-030-58309-5_10

• Schilling, Frank-Peter; Stadelmann, Thilo, eds.,

  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.

  Available from: 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.

  Available from: 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.

  Available from: 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.

  pp. 31-36.

  Available from: 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.

  pp. 13-14.

  Available from: 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.

  pp. 333-345.

  Lecture Notes in Computer Science ; 11081.

  Available from: 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.

  pp. 17-38.

  Lecture Notes in Computer Science ; 11081.

  Available from: 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.

  Available from: 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.

  pp. 1-6.

  Available from: 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.

  pp. 126-138.

  Lecture Notes in Computer Science ; 11081.

  Available from: 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.

  pp. 3549-3554.

  Available from: 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.

  pp. 346-358.

  Lecture Notes in Computer Science ; 11081.

  Available from: 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.

  Available from: 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.

  Available from: 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.

  Available from: 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.

  Available from: 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.

• 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.

  Available from: https://doi.org/10.21256/zhaw-28651

• von der Malsburg, Christoph; Stadelmann, Thilo; Grewe, Benjamin F.,

  2022.

  A theory of natural intelligence.

  arXiv.

  Available from: 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.

  Available from: https://doi.org/10.21256/zhaw-20885

• Stadelmann, Thilo; Schilling, Frank-Peter,

  2019.

  Deep Learning in medizinischer Diagnostik und Qualitätskontrolle.

  Netzwoche.

  Available from: 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.

  Available from: 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), pp. 32-33.

  Available from: https://doi.org/10.21256/zhaw-3643

• 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.

  Available from: 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.

  Available from: https://stdm.github.io/downloads/papers/KogWis_2022.pdf

Publications before appointment at the 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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