Prof. Thilo Stadelmann , Dr. rer. nat.

Work at ZHAW

Position

• Professor of artificial intelligence and machine learning (chair of Engineering - Information)
• Director of Centre, Centre for Artificial Intelligence
• Head of Machine, Perception & Cognition Group
• Scientist
• Principal investigator
• Keynote speaker
• Lecturer, consultant, networker, mentor, leader, father, believer

Focus

• Artificial intelligence and machine learning: deep learning, pattern recognition, computer vision, audio analysis, intelligent sysetems
• Research interests: Robust and practical deep learning, industrial computer vision, medical imaging, more general AI, trustworthy AI, learning to learn, document recognition, voice recognition, data science
• Data Science

Teaching

• Lecturer in the bachelor programmes computer science and data science, in continuing education, and on the master and PhD level
• Guest lecturer at several universities nationally and internationally
• BSc module Artificial Intelligence 1&2
• MSc module Machine Learning
• Supervisor of student theses BSc/MSc/continuing education
• PhD supervisor

Professional development teaching

• CAS Information Engineering
• CAS Machine Intelligence

Experience

• Founder, member of the board
  AlpineAI AG
  07 / 2023 - today
• Keynote speaker
  Premium Speakers Agency
  05 / 2023 - today
• Professor of AI/ML, director of ZHAW CAI, head of MPC Group
  ZHAW School of Engineering
  04 / 2021 - today
• Co-founder and member of the board
  ZHAW Datalab
  05 / 2013 - today
• Member of the Board
  Data innovation alliance
  01 / 2016 - 11 / 2023
• Scientific director
  ZHAW digital
  01 / 2019 - 03 / 2021
• Professor of computer science
  ZHAW Institute of Applied Information Technology
  07 / 2018 - 03 / 2021
• Deputy head of research area information engineering
  ZHAW Institute of applied Information Technology
  06 / 2015 - 03 / 2021
• Head
  ZHAW Datalab
  05 / 2013 - 12 / 2018
• Vice president
  Swiss Group of Artificial Intelligence and Cognitive Science (SGAICO)
  02 / 2014 - 09 / 2018
• Senior lecturer for information engineering
  ZHAW Institute of applied Information Technology
  02 / 2013 - 06 / 2018
• Managing director ad interim
  Swiss Alliance for Data-Intensive Services
  08 / 2017 - 02 / 2018
• Co-organizer
  Zurich Machine Learning & Data Science Meetup
  09 / 2015 - 03 / 2017
• Head of smart software
  TWT GmbH Science & Innovation
  04 / 2011 - 06 / 2013
• Director of internal IT
  TWT GmbH Science & Innovation
  04 / 2012 - 01 / 2013
• Software architect and project leader
  TWT GmbH Science & Innovation
  07 / 2010 - 03 / 2011
• Research assistant in the area of audio- and video mining
  Philipps-University of Marburg
  09 / 2004 - 06 / 2010
• Several sideline jobs in software development and data mining
  diverse
  02 / 1998 - 06 / 2010

Education and Continuing education

Education

• Dr. rer. nat. (equivalent PhD) / computer science
  Philipps-University Marburg
  08 / 2004 - 07 / 2010
• Dipl. Inform. (FH) (equivalent MSc) / Computer science with focus technical/scientific applications
  Giessen-Friedberg University of Applied Sciences
  09 / 2000 - 07 / 2004

Continuing Education

• Management education ZHAW
  Zurich University of Applied Sciences
  06 / 2018
• CAS Higher education didactics
  Zurich University of Teacher Education
  06 / 2015

Network

Membership of networks

• Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE)
• European Centre for Living Technology (ECLT) (fellow)
• IEEE CS, CIS, SMC (senior member)
• International Association for Pattern Recognition (IAPR) (member of TC3)
• German Association for Pattern Recognition (DAGM)
• Gesellschaft für Klassifikation - Data Science Society (GfKl)
• Data innovation alliance (d+i) (Co-Founder)
• Premium Speakers Agency
• ZHAW Datalab (member of the board)
• ZHAW staff prayer meeting

ORCID digital identifier

ORCID ID: 0000-0002-3784-0420

Awards

• Finalist - "The Pascal"
  Digital Economy Award
  09 / 2024
• Honorable Mention - Best Paper Award
  IEEE Swiss Conference on Data Science 2024
  06 / 2024
• Supervisor of several award-winning student theses (u.a. ZHAW SGD Award, Siemens Excellence Award, Lab Sciences Award, Dr. Waldemar Jucker Award)
  various
  01 / 2024
• Most Cited Paper Award for "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
• Teaching award "Best Teaching - Best Practices"
  ZHAW
  09 / 2019

Social media

LinkedIn

Media presence

• Frequent media coverage, e.g., as interviewee
• What power has AI? (Keynote at KCF'2023)
• Suddenly useful: ChatGPT heralds tech revolution (Finanzen und Wirtschaft)
• How dangerous will AI be for us? Three horror scenarios in the fact check (20 Minuten)
• ChatGPT all-rounder: how self-determined is artificial intelligence? (SwissLife Select Blog)
• Artificial intelligence: here comes the 'Cambrian explosion' (NZZ am Sonntag)
• Human 2.0 (Fenster zum Sonntag)

Projects

• Smart urban green spaces / Team member / laufend
• AI for REAL-world NETwork operation / Team member / laufend
• Stability of self-organizing net fragments as inductive bias for next-generation deep learning / Project leader / laufend
• Machine Learning for Body Composition Analysis / Project leader / laufend
• certAInty – A Certification Scheme for AI systems / Team member / laufend
• LINA: Shared Large-scale Infrastructure for the Development and Safe Testing of Autonomous Systems / Project leader / laufend
• AC3T – AI powered CBCT for improved Combination Cancer Therapy / Team member / laufend
• Accessible Scientific PDFs for All / Co-project leader / laufend
• European Conference Series on Artificial Intelligence in Industry and Finance / Team member / laufend
• Deep Dive ML on Simulated Enzyme-Electrolysis Performance / Project leader / abgeschlossen
• Enabling Scientific Diplomacy: Preparation of the GESDA Neurotechnology Compass / Deputy project leader / abgeschlossen
• Master3D – 3D-Master for a Digitized Manufacturing Platform / Deputy project leader / abgeschlossen
• DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning / Project leader / abgeschlossen
• Mobile Inclusion Lab / Co-project leader / abgeschlossen
• AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support / Co-project leader / abgeschlossen
• TAILOR – Trustworthy and sample efficient vision transformers / Co-project leader / abgeschlossen
• Pilot study machine learning for injection molding processes / Project leader / abgeschlossen
• DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes / Team member / abgeschlossen
• TAILOR – Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization / Project leader / abgeschlossen
• RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Co-project leader / abgeschlossen
• FWA: Visual Food Waste Analysis for Sustainable Kitchens / Co-project leader / abgeschlossen
• Feasibility Study Reinforcement Learning for Heating Systems / Team member / abgeschlossen
• FarmAI – Artificial intelligence for Farming Simulator / Team member / abgeschlossen
• Libra: A One-Tool Solution for MLD4 Compliance / Team member / abgeschlossen
• Deep-Learning-basierter Spracherkenner mit beschränkten Trainingsdaten (DeLLA) / Team member / abgeschlossen
• DeepText: Intelligent Text Analysis with Deep Learning / Team member / abgeschlossen
• DeepScore: Digital Music Stand with Musical Understanding via Active Sheet Technology / Project leader / abgeschlossen
• Complexity 4.0 / Deputy project leader / abgeschlossen
• Automated Article Segmentation of Newspaper Pages for "Real Time Print Media Monitoring" (PANOPTES) / Team member / abgeschlossen
• MobileMall / Project leader / abgeschlossen
• SODES – Swiss Open Data Exploration System / Team member / abgeschlossen
• Talkalyzer / Team member / abgeschlossen

Publications

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

  2024.

  Hierarchical glocal attention pooling for graph classification.

  Pattern Recognition Letters.

  186, pp. 71-77.

  Available from: 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), pp. 16389.

  Available from: 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).

  Available from: 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, pp. 76963-76974.

  Available from: 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, pp. 64-69.

  Available from: 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, pp. 3768-3789.

  Available from: 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), pp. 1-14.

  Available from: 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.

  Available from: 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), pp. 6228-6242.

  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.

  1(3), pp. 036108.

  Available from: 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), 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,

  2024.

  Wegweiser Künstliche Intelligenz : verstehen, anwenden und zuversichtlich Zukunft gestalten

  .

  In:

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

  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, 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

• 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.

  Available from: 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.

  pp. 138-145.

  Available from: 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.

  pp. 229-232.

  Available from: 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.

  Available from: https://doi.org/10.1109/SDS60720.2024.00036

• 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.

  Available from: 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.

  pp. MTu4A.5.

  Available from: 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.

  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.

  Swiss Group for Artificial Intelligence and Cognitive Science.

  Available from: https://doi.org/10.21256/zhaw-7739

• 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

• Lanfant, Briac; Lack, Silvan; Meyer, Benjamin; Abdulkadir, Ahmed; Stadelmann, Thilo; Schmid, Daniel,

  2024.

  3D-master-based method for optimizing the cost calculatin of PBF-LB/M manufactured parts.

  In:

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

• 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.
• Thilo Stadelmann. "Voice Modeling Methods for Automatic Speaker Recognition". Dissertation, Philipps-Universität Marburg. Available online, 2010.
• 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.
• 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.
• 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.
• Thilo Stadelmann and Bernd Freisleben. "On the MixMax Model and Cepstral Features for Noise-Robust Voice Recognition". Technical Report, Marburg University, July 2010.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Other publications

• Bibliometrics on Google Scholar
• Regular appearances as a speaker and panellist at congresses and industry events (see below for selected examples)
• Keynote: KSA Research & Innovation Day 2024
• Keynote: Oracle Data & AI Forum 2024
• Keynote: Schweizer Bibliothekskongress 2023
• Keynote: Direct Day 2023
• Deep Learning in the wild (Invited Talk @ ANNPR 2018)
• Keynote: Hospitality Summit 2024

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