Prof. Thilo Stadelmann, Dr. rer. nat.

Work at ZHAW

Position

• Professor of artificial intelligence and machine learning
• Director of Centre, Centre for Artificial Intelligence
• Head of Machine, Perception & Cognition research group

Focus

• Artificial intelligence and machine learning as well as their societal effects
• Research interests: Sensory pattern recognition with deep learning; document recognition and multimedia analysis (e.g., for industry and medicine); biology-inspired neural system development and world models; societal effects of AI and pro-human AI design
• Real-world AI tasks often start with the detection of patterns in sensory data (like similarities and anomalies in image, video, time series, or audio data) and extend to making sense of these patterns (leading to actions like a classification, segmentation, prediction or decision). The MPC group spans this arc from perception to cognition, being rooted in pattern recognition research and focusing on deep neural network methodology. Tasks we study have diffferent learning target (e.g., detection, classification, clustering, segmentation, novelty detection, control) and corresponding practical use case (e.g., predictive maintenance for industrial assets, speaker recognition for multimedia indexing, document analysis for construction, computer vision for industrial quality control, automated machine learning in general data sciences, deep reinforcement learning for building control, video analysis for automatic media production, face recognition for biometric access control, human-AI co-learning for meaningful human-AI collaboration in high-consequence scenarios), which in turn shed light on different aspects of the learning process. We use this experience to create increasingly general real-world AI systems built on neural architectures. Fundamental challenges thereby lie in the systems’ robustness as well as their sample and label efficiency, and are often approached with transfer learning and domain adaptation. Beyond this, we take inspiration from biological learning to work on next-level AI methodology with world models, and are actively engaged at the interface of technology and society to contribute to a future worth living in with pro-human AI.

Teaching

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

Professional development teaching

• CAS KI verstehen und anwenden: No-Code Lösungen für die betriebliche Praxis
• CAS Information Engineering
• CAS Physical AI & Robotics

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, director 2013-2018
  ZHAW Datalab
  05 / 2013 - today
• Co-founder, member of the Board, managing director 2017-2018
  Data innovation alliance
  01 / 2016 - 11 / 2023
• Scientific director
  ZHAW digital
  01 / 2019 - 03 / 2021
• Deputy head of research area information engineering, professor of computer science since 2018
  ZHAW Institute of applied Information Technology
  06 / 2015 - 03 / 2021
• Senior lecturer for information engineering
  ZHAW Institute of applied Information Technology
  02 / 2013 - 06 / 2018
• Software architect and project leader, team lead smart software (since 2011), head of IT (since 2012)
  TWT GmbH Science & Innovation
  07 / 2010 - 01 / 2013
• Research assistant in the area of audio- and video mining
  Philipps-University of Marburg
  09 / 2004 - 06 / 2010
• Several side 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 (CAIRNE)
• 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)
• ZHAW Datalab (member of the board)
• Swiss Centre for Responsible AI (Co-Founder)
• ZHAW staff prayer meeting

ORCID digital identifier

ORCID ID: 0000-0002-3784-0420

Awards

• Best Scientific Full Paper Award
  IEEE Swiss Conference on Data Science 2025
  06 / 2025
• 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 Swiss Conference on Data Science 2023
  06 / 2023
• Impact Award 2022
  ZHAW digital
  12 / 2022
• DIZH Fellowship 2022
  ZHAW digital
  11 / 2022
• Best Paper Award
  Swiss Conference on Data Science 2021
  06 / 2021
• Best Poster Presentation Award
  Swiss Conference on Data Science 2020
  06 / 2020
• Teaching award "Best Teaching - Best Practices"
  ZHAW
  09 / 2019

Social media

• LinkedIn
• YouTube

Media presence

• Regular contributions to public media outlets (e.g., SRF 10 vor 10, NZZ, 20 Minutes), interviews, and podcasts
• Regular appearances as a speaker and panellist at scientific conferences, congresses and industry events (key notes, invited talks, etc.)
• Talks and interviews available online (including TEDx)
• Event Booking

Projects

• Automated Music Track Extraction and Identification from Full-Length Movies / Deputy project leader / ongoing
• Avatar Demonstrator: VR Control for Humanoid Robots / Co-project leader / ongoing
• SCRAI – A Think-and-Do-Tank for Responsible Development and Societal Alignment of Artificial Intelligence Systems / Team member / ongoing
• dAIrector – Automated multi-camera live production for events / Project leader / ongoing
• Evidence-Based Diagnostic Assistance for Echocardiography / Deputy project leader / ongoing
• AI for REAL-world NETwork operation / Team member / ongoing
• LINA: Shared Large-scale Infrastructure for the Development and Safe Testing of Autonomous Systems / Project leader / ongoing
• Study on semi-automatic poster cataloguing at the Swiss National Library / Project leader / completed
• Smart urban green spaces / Team member / completed
• Deep Dive ML on Simulated Enzyme-Electrolysis Performance / Project leader / completed
• Stability of self-organizing net fragments as inductive bias for next-generation deep learning / Project leader / completed
• Consulting Service for the preparation of the GESDA Anticipation Observatory / Deputy project leader / completed
• Machine Learning for Body Composition Analysis / Project leader / completed
• 3D-Master for a Digitized Manufacturing Platform / Deputy project leader / completed
• certAInty – A Certification Scheme for AI systems / Team member / completed
• DISTRAL: Industrial Process Monitoring for Injection Molding with Distributed Transfer Learning / Project leader / completed
• Mobile Inclusion Lab / Co-project leader / completed
• AI powered CBCT for improved Combination Cancer Therapy / Team member / completed
• AUTODIDACT – Automated Video Data Annotation to Empower the ICU Cockpit Platform for Clinical Decision Support / Co-project leader / completed
• TAILOR – Trustworthy and sample efficient vision transformers / Co-project leader / completed
• Pilot study machine learning for injection molding processes / Project leader / completed
• Accessible Scientific PDFs for All / Co-project leader / completed
• European Conference Series on Artificial Intelligence in Industry and Finance / Team member / completed
• DIR3CT: Deep Image Reconstruction through X-Ray Projection-based 3D Learning of Computed Tomography Volumes / Team member / completed
• TAILOR – Foundations of Trustworthy AI - Integrating Reasoning, Learning and Optimization / Project leader / completed
• RealScore – Scanning of Real-World Sheet Music for a Digital Music Stand / Co-project leader / completed
• Visual Food Waste Analysis for Sustainable Kitchens / Co-project leader / completed
• Feasibility Study Reinforcement Learning for Heating Systems / Team member / completed
• FarmAI – Artificial intelligence for Farming Simulator / Team member / completed
• Libra: A One-Tool Solution for MLD4 Compliance / Team member / completed
• Deep-Learning-basierter Spracherkenner mit beschränkten Trainingsdaten / Team member / completed
• DeepText: Intelligent Text Analysis with Deep Learning / Team member / completed
• DeepScore: Digital Music Stand with Musical Understanding via Active Sheet Technology / Project leader / completed
• Complexity 4.0 / Deputy project leader / completed
• Automated Article Segmentation of Newspaper Pages for "Real Time Print Media Monitoring" / Team member / completed
• MobileMall / Project leader / completed
• SODES – Swiss Open Data Exploration System / Team member / completed
• Talkalyzer / Team member / completed

Selected publications

• Stadelmann, T., Merkt, P. H., & Barr, K. (2026). The stochastic nature of machine learning and its implications for high-consequence AI. AI and Ethics, 6(2), 195. https://doi.org/10.1007/s43681-026-01042-1
• Saponati, M., Sager, P., Aceituno, P. V., Stadelmann, T., & Grewe, B. (2025). The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training [Conference paper]. In A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, K. Wagstaff, & J. Zhu (Eds.), Proceedings of the 42nd International Conference on Machine Learning (pp. 52958–52994). Proceedings of Machine Learning Research. https://doi.org/10.21256/zhaw-33652
• Tuggener, L., Emberger, R., Ghosh, A., Sager, P., Satyawan, Y. P., Montoya, J., Goldschagg, S., Seibold, F., Gut, U., Ackermann, P., Schmidhuber, J., & Stadelmann, T. (2024). Real world music object recognition. Transactions of the International Society for Music Information Retrieval, 7(1), 1–14. https://doi.org/10.5334/tismir.157
• Segessenmann, J., Stadelmann, T., Davison, A., & Dürr, O. (2023). Assessing deep learning : a work program for the humanities in the age of artificial intelligence. AI and Ethics, 5(1), 1–32. https://doi.org/10.1007/s43681-023-00408-z
• Lukic, Y., Vogt, C., Dürr, O., & Stadelmann, T. (2016). Speaker identification and clustering using convolutional neural networks. 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),. https://doi.org/10.1109/MLSP.2016.7738816

Publications

Articles in scientific journal, peer-reviewed

• Ali, W., Vascon, S., Stadelmann, T., & Pelillo, M. (2026). Multi-view graph pooling via dominant sets for graph classification. Pattern Recognition, 172, Part D(112786). https://doi.org/10.1016/j.patcog.2025.112786
• Sager, P. J., Meyer, B., Yan, P., von Wartburg-Kottler, R., Etaiwi, L., Enayati, A., Nobel, G., Abdulkadir, A., Grewe, B. F., & Stadelmann, T. (2026). A comprehensive survey of agents for computer use : foundations, challenges, and future directions. Journal of Artificial Intelligence Research, 85(34). https://doi.org/10.1613/jair.1.19490
• Stadelmann, T., Merkt, P. H., & Barr, K. (2026). The stochastic nature of machine learning and its implications for high-consequence AI. AI and Ethics, 6(2), 195. https://doi.org/10.1007/s43681-026-01042-1
• Bolck, H., Vollenweider, J., Merkli, F., Barden, A., Jajcay, M., Trempeck, P., Rafailović, B., Fraefel, R., Lenhart, P. M., Chavarriaga, R., Renold, M., Bogojeska, J., Stadelmann, T., & Guillaume, M. (2026). LINA’s testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs). Frontiers in Robotics and AI, 13(1764248). https://doi.org/10.3389/frobt.2026.1764248
• Sager, P. J., Deriu, J. M., Grewe, B. F., Stadelmann, T., & von der Malsburg, C. (2026). The cooperative network architecture : learning structured networks as representation of sensory patterns. Neural Computation. https://doi.org/10.1162/neco.a.1505
• Bolck, H., Vollenweider, J., Merkli, F., Barden, A., Jajcay, M., Trempeck, P., Rafailović, B., Lenhart, P. M., Chavarriaga, R., Renold, M., Bogojeska, J., Stadelmann, T., & Guillaume, M. (2026). Perspective: LINA’s testing infrastructure enables AI to take-off in unmanned aerial vehicles (UAVs). Frontiers in Robotics and AI. https://doi.org/10.21256/zhaw-35722
• Tuggener, L., Stadelmann, T., & Schmidhuber, J. (2025). Efficient rotation invariance in deep neural networks through artificial mental rotation. Frontiers in Computer Science, 7(1644044). https://doi.org/10.3389/fcomp.2025.1644044
• Mussi, M., Metelli, A. M., Restelli, M., Losapio, G., Bessa, R. J., Boos, D., Borst, C., Leto, G., Castagna, A., Chavarriaga, R., Dias, D., Egli, A., Eisenegger, A., El Manyari, Y., Fuxjäger, A., Geraldes, J., Hamouche, S., Hassouna, M., Lemetayer, B., et al. (2025). Human-AI interaction in safety-critical network infrastructures. iScience, 28(9), 113400. https://doi.org/10.1016/j.isci.2025.113400
• Stadelmann, T. (2025). Evidence-based AI risk assessment for public policy. Public Money & Management, 46(1), 5–7. https://doi.org/10.1080/09540962.2025.2541304
• Tuggener, L., Emberger, R., Ghosh, A., Sager, P., Satyawan, Y. P., Montoya, J., Goldschagg, S., Seibold, F., Gut, U., Ackermann, P., Schmidhuber, J., & Stadelmann, T. (2024). Real world music object recognition. Transactions of the International Society for Music Information Retrieval, 7(1), 1–14. https://doi.org/10.5334/tismir.157
• Ali, W., Vascon, S., Stadelmann, T., & Pelillo, M. (2024). Hierarchical glocal attention pooling for graph classification. Pattern Recognition Letters, 186, 71–77. https://doi.org/10.1016/j.patrec.2024.09.009
• Jermain, P. R., Oswald, M., Langdun, T., Wright, S., Khan, A., Stadelmann, T., Abdulkadir, A., & Yaroslavsky, A. N. (2024). Deep learning-based cell segmentation for rapid optical cytopathology of thyroid cancer. Scientific Reports, 14(1), 16389. https://doi.org/10.1038/s41598-024-64855-2
• Dashti, A., Stadelmann, T., & Kohl, T. (2024). Machine learning for robust structural uncertainty quantification in fractured reservoirs. Geothermics, 120(103012). https://doi.org/10.1016/j.geothermics.2024.103012
• Schmitt-Koopmann, F., Huang, E. M., Hutter, H.-P., Stadelmann, T., & Darvishy, A. (2024). MathNet : a data-centric approach for printed mathematical expression recognition. IEEE Access, 12, 76963–76974. https://doi.org/10.1109/ACCESS.2024.3404834
• Neururer, D., Dellwo, V., & Stadelmann, T. (2024). Deep neural networks for automatic speaker recognition do not learn supra-segmental temporal features. Pattern Recognition Letters, 181, 64–69. https://doi.org/10.1016/j.patrec.2024.03.016
• Yan, P., Abdulkadir, A., Luley, P.-P., Rosenthal, M., Schatte, G. A., Grewe, B. F., & Stadelmann, T. (2024). A comprehensive survey of deep transfer learning for anomaly detection in industrial time series : methods, applications, and directions. IEEE Access, 12, 3768–3789. https://doi.org/10.1109/ACCESS.2023.3349132
• Battaglia, M., Comi, E., Stadelmann, T., Hiestand, R., Ruhstaller, B., & Knapp, E. (2023). Deep ensemble inverse model for image-based estimation of solar cell parameters. APL Machine Learning, 1(3), 36108. https://doi.org/10.1063/5.0139707
• Segessenmann, J., Stadelmann, T., Davison, A., & Dürr, O. (2023). Assessing deep learning : a work program for the humanities in the age of artificial intelligence. AI and Ethics, 5(1), 1–32. https://doi.org/10.1007/s43681-023-00408-z
• Amirian, M., Montoya-Zegarra, J. A., Herzig, I., Eggenberger Hotz, P., Lichtensteiger, L., Morf, M., Züst, A., Paysan, P., Peterlik, I., Scheib, S., Füchslin, R. M., Stadelmann, T., & Schilling, F.-P. (2023). Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks. Medical Physics, 50(10), 6228–6242. https://doi.org/10.1002/mp.16405
• Tuggener, L., Schmidhuber, J., & Stadelmann, T. (2022). Is it enough to optimize CNN architectures on ImageNet? Frontiers in Computer Science, 4(1041703). https://doi.org/10.3389/fcomp.2022.1041703
• Schmitt-Koopmann, F. M., Huang, E. M., Hutter, H.-P., Stadelmann, T., & Darvishy, A. (2022). FormulaNet : a benchmark dataset for mathematical formula detection. IEEE Access, 10, 91588–91596. https://doi.org/10.1109/ACCESS.2022.3202639
• Sager, P., Salzmann, S., Burn, F., & Stadelmann, T. (2022). Unsupervised domain adaptation for vertebrae detection and identification in 3D CT volumes using a domain sanity loss. Journal of Imaging, 8(8), 222. https://doi.org/10.3390/jimaging8080222
• Schilling, F.-P., Flumini, D., Füchslin, R. M., Gavagnin, E., Geller, A., Quarteroni, S., & Stadelmann, T. (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). https://doi.org/10.5445/IR/1000146422
• Stadelmann, T., Klamt, T., & Merkt, P. H. (2022). Data centrism and the core of Data Science as a scientific discipline. Archives of Data Science, Series A, 8(2). https://doi.org/10.5445/IR/1000143637
• Wehrli, S., Hertweck, C., Amirian, M., Glüge, S., & Stadelmann, T. (2021). Bias, awareness, and ignorance in deep-learning-based face recognition. AI and Ethics, 2(3), 509–522. https://doi.org/10.1007/s43681-021-00108-6
• Stadelmann, T., Keuzenkamp, J., Grabner, H., & Würsch, C. (2021). The AI-Atlas : didactics for teaching AI and machine learning on-site, online, and hybrid. Education Sciences, 11(7), 318. https://doi.org/10.3390/educsci11070318
• Tuggener, L., Amirian, M., Benites de Azevedo e Souza, F., von Däniken, P., Gupta, P., Schilling, F.-P., & Stadelmann, T. (2020). Design patterns for resource-constrained automated deep-learning methods. Ai, 1(4), 510–538. https://doi.org/10.3390/ai1040031
• Dessimoz, J.-D., Koehler, J., & Stadelmann, T. (2015). AI in Switzerland. AI Magazine, 36(2), 102–105. https://doi.org/10.1609/aimag.v36i2.2591
• Stockinger, K., & Stadelmann, T. (2014). Data Science für Lehre, Forschung und Praxis. HMD Praxis der Wirtschaftsinformatik, 51(4), 469–479. https://doi.org/10.1365/s40702-014-0040-1

Books, peer-reviewed

• Stadelmann, T., & Schilling, F.-P. (2022). Advances in deep neural networks for visual pattern recognition. MDPI. https://www.mdpi.com/journal/jimaging/special_issues/deep_neural_network
• Schilling, F.-P., & Stadelmann, T. (2020). Artificial neural networks in pattern recognition. MDPI. https://www.mdpi.com/journal/computers/special_issues/ANNPR2020
• Braschler, M., Stadelmann, T., & Stockinger, K. (2019). Applied data science : lessons learned for the data-driven business (1. Auflage). Springer. https://doi.org/10.1007/978-3-030-11821-1

Book chapters, peer-reviewed

• Stadelmann, T. (2025). Wegweiser Künstliche Intelligenz : verstehen, anwenden und zuversichtlich Zukunft gestalten. In S. Hersberger & C. H. Hoffmann (eds.), Wie die Künstliche Intelligenz die Wirtschaft verändert : Überblick über die theoretischen Grundlagen und Praxisbeispiele entlang von Ökosystemen (pp. 3–17). Springer. https://doi.org/10.1007/978-3-658-46839-2_1
• Meierhofer, J., Stadelmann, T., & Cieliebak, M. (2019). Data products. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 47–61). Springer. https://doi.org/10.1007/978-3-030-11821-1_4
• Hollenstein, L., Lichtensteiger, L., Stadelmann, T., Amirian, M., Budde, L., Meierhofer, J., Füchslin, R. M., & Friedli, T. (2019). Unsupervised learning and simulation for complexity management in business operations. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 313–331). Springer. https://doi.org/10.1007/978-3-030-11821-1_17
• Stadelmann, T. (2019). Wie maschinelles Lernen den Markt verändert. In R. Haupt & S. Schmitz (eds.), Digitalisierung: Datenhype mit Werteverlust? : ethische Perspektiven für eine Schlüsseltechnologie (pp. 67–79). SCM Hänssler. https://doi.org/10.21256/zhaw-18822
• Braschler, M., Stadelmann, T., & Stockinger, K. (2019). Data science. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 17–29). Springer. https://doi.org/10.1007/978-3-030-11821-1_2
• Stockinger, K., Braschler, M., & Stadelmann, T. (2019). Lessons learned from challenging data science case studies. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 447–465). Springer. https://doi.org/10.1007/978-3-030-11821-1_24
• Stadelmann, T., Stockinger, K., Heinatz-Bürki, G., & Braschler, M. (2019). Data scientists. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 31–45). Springer. https://doi.org/10.1007/978-3-030-11821-1_3
• Stadelmann, T., Tolkachev, V., Sick, B., Stampfli, J., & Dürr, O. (2019). Beyond ImageNet : deep learning in industrial practice. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 205–232). Springer. https://doi.org/10.1007/978-3-030-11821-1_12
• Stadelmann, T., Braschler, M., & Stockinger, K. (2019). Introduction to applied data science. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 3–16). Springer. https://doi.org/10.1007/978-3-030-11821-1_1
• Stockinger, K., Stadelmann, T., & Ruckstuhl, A. (2016). Data Scientist als Beruf. In D. Fasel & M. Andreas (eds.), Big Data (pp. 59–81). Springer. https://doi.org/10.1007/978-3-658-11589-0_4

Written conference contributions, peer-reviewed

• Stadelmann, T., Heitz, C., von Wartburg-Kottler, R., & Schärer, A. L. (2026, May 21). Pro-human AI design : concept, methodology, and preliminary results. uDay XXIV : #Responsible AI, Dornbirn, Austria, 21 May 2026. https://doi.org/10.21256/zhaw-36392
• Tuggener, L., Spalinger, S., & Stadelmann, T. (2026, May 6). From POC to production : how to build a support ticket triage system that survives contact with the real world. 13th IEEE Swiss Conference on Data Science and AI (SDS), Zurich, Switzerland, 6-7 May 2026.
• Ali, W., Stämpfli, S., Stadelmann, T., Aschkenasy, S., & Abdulkadir, A. (2026, April 8). Automated, vendor-agnostic measurement of myocardial tissue velocities in echocardiography. 23rd IEEE International Symposium on Biomedical Imaging (ISBI), London, United Kingdom, 8-11 April 2026. https://doi.org/10.21256/zhaw-35211
• Ali, W., Vascon, S., Stadelmann, T., & Pelillo, M. (2026, January 23). Topology-aware node dropping augmentation for graph classification. 6th International Conference on Neural Networks, Information and Communication Engineering (NNICE), Hefei, China, 23-25 January 2026. https://doi.org/10.21256/zhaw-35188
• Sager, P., Kamaraj, A., Grewe, B. F., & Stadelmann, T. (2025). Deep retrieval at CheckThat! 2025 : identifying scientific papers from implicit social media mentions via hybrid retrieval and re-ranking [Conference paper]. In G. Faggioli, N. Ferro, P. Rosso, & D. Spina (Eds.), Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2025) (pp. 1141–1155). CEUR Workshop Proceedings. https://doi.org/10.21256/zhaw-33534
• Meyer, B., Sager, P., Abdulkadir, A., Grewe, B. F., Schuetz, P., Stadelmann, T., & Burn, F. (2025). Hounsfield unit ranges as inductive bias for intra-clinical learning of data-efficient CT segmentation models [Conference paper]. 2025 IEEE Swiss Conference on Data Science (SDS), 1–7. https://doi.org/10.1109/SDS66131.2025.00008
• Saponati, M., Sager, P., Aceituno, P. V., Stadelmann, T., & Grewe, B. (2025). The underlying structures of self-attention : symmetry, directionality, and emergent dynamics in Transformer training [Conference paper]. In A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, K. Wagstaff, & J. Zhu (Eds.), Proceedings of the 42nd International Conference on Machine Learning (pp. 52958–52994). Proceedings of Machine Learning Research. https://doi.org/10.21256/zhaw-33652
• Yan, P., Abdulkadir, A., Schatte, G. A., Aguzzi, G., Gha, J., Pascher, N., Rosenthal, M., Gao, Y., Grewe, B. F., & Stadelmann, T. (2025). Learning actionable world models for industrial process control [Conference paper]. 2025 IEEE Swiss Conference on Data Science (SDS), 111–118. https://doi.org/10.1109/SDS66131.2025.00022
• Lanfant, B., Lack, S., Meyer, B., Abdulkadir, A., Stadelmann, T., & Schmid, D. (2025). 3D-master-based method for optimizing the cost calculation of PBF-LB/M manufactured parts [Conference paper]. BHM Berg- Und Hüttenmännische Monatshefte, 170(3), 158–171. https://doi.org/10.1007/s00501-025-01563-y
• Begga, A., Ali, W., Niculescu, G., Escolano, F., Stadelmann, T., & Pelillo, M. (2025). Community-hop : enhancing node classification through community preference [Conference paper]. In A. Torsello, L. Rossi, L. Cosmo, & G. Minello (Eds.), Structural, Syntactic, and Statistical Pattern Recognition (pp. 21–30). Springer. https://doi.org/10.1007/978-3-031-80507-3_3
• Bolt, P., Ziebart, V., Jaeger, C., Schmid, N., Stadelmann, T., & Füchslin, R. M. (2024). A simulation study on energy optimization in building control with reinforcement learning [Conference paper]. In C. Y. Suen, A. Krzyzak, M. Ravanelli, E. Trentin, C. Subakan, & N. Nobile (Eds.), Artificial Neural Networks in Pattern Recognition. Springer. https://doi.org/10.1007/978-3-031-71602-7_27
• Meyer, B., Stadelmann, T., & Lüthi, M. (2024). ScalaGrad : a statically typed automatic differentiation library for safer data science [Conference paper]. 2024 11th IEEE Swiss Conference on Data Science (SDS), 229–232. https://doi.org/10.1109/SDS60720.2024.00040
• Yan, P., Abdulkadir, A., Aguzzi, G., Schatte, G. A., Grewe, B. F., & Stadelmann, T. (2024). Automated process monitoring in injection molding via representation learning and setpoint regression [Conference paper]. 2024 11th IEEE Swiss Conference on Data Science (SDS), 138–145. https://doi.org/10.1109/SDS60720.2024.00027
• Tuggener, L., Sager, P., Taoudi-Benchekroun, Y., Grewe, B. F., & Stadelmann, T. (2024, September 18). So you want your private LLM at home? : a survey and benchmark of methods for efficient GPTs. 2024 11th IEEE Swiss Conference on Data Science (SDS). https://doi.org/10.1109/SDS60720.2024.00036
• Jermain, P. R., Oswald, M., Langdun, T., Wright, S., Khan, A., Stadelmann, T., Abdulkadir, A., & Yaroslavsky, A. N. (2024). Rapid optical cytology with deep learning-based cell segmentation for diagnosis of thyroid lesions [Conference paper]. Optica Biophotonics Congress: Biomedical Optics 2024 (Translational, Microscopy, OCT, OTS, BRAIN), MTu4A. https://doi.org/10.1364/MICROSCOPY.2024.MTu4A.5
• Ali, W., Vascon, S., Stadelmann, T., & Pelillo, M. (2023). Quasi-CliquePool : hierarchical graph pooling for graph classification [Conference paper]. SAC ’23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 544–552. https://doi.org/10.1145/3555776.3578600
• Emberger, R., Boss, J. M., Baumann, D., Seric, M., Huo, S., Tuggener, L., Keller, E., & Stadelmann, T. (2023). Video object detection for privacy-preserving patient monitoring in intensive care [Conference paper]. 2023 10th IEEE Swiss Conference on Data Science (SDS), 85–88. https://doi.org/10.1109/SDS57534.2023.00019
• Luley, P.-P., Deriu, J. M., Yan, P., Schatte, G. A., & Stadelmann, T. (2023). From concept to implementation : the data-centric development process for AI in industry [Conference paper]. 2023 10th IEEE Swiss Conference on Data Science (SDS), 73–76. https://doi.org/10.1109/SDS57534.2023.00017
• Herzig, I., Paysan, P., Scheib, S., Züst, A., Schilling, F.-P., Montoya, J., Amirian, M., Stadelmann, T., Eggenberger Hotz, P., Füchslin, R. M., & Lichtensteiger, L. (2022). Deep learning-based simultaneous multi-phase deformable image registration of sparse 4D-CBCT [Conference poster]. Medical Physics, 49(6), e325–e326. https://doi.org/10.1002/mp.15769
• Tuggener, L., Satyawan, Y. P., Pacha, A., Schmidhuber, J., & Stadelmann, T. (2021). The DeepScoresV2 dataset and benchmark for music object detection [Conference paper]. 2020 25th International Conference on Pattern Recognition (ICPR), 9188–9195. https://doi.org/10.1109/ICPR48806.2021.9412290
• Amirian, M., Montoya, J., Gruss, J., Stebler, Y. D., Bozkir, A. S., Calandri, M., Schwenker, F., & Stadelmann, T. (2021, October). PrepNet : a convolutional auto-encoder to homogenize CT scans for cross-dataset medical image analysis. Proceedings of CISP-BMEI’21. https://doi.org/10.21256/zhaw-23318
• Knapp, E., Battaglia, M., Stadelmann, T., Jenatsch, S., & Ruhstaller, B. (2021). XGBoost trained on synthetic data to extract material parameters of organic semiconductors [Conference paper]. Proceedings of the 8th SDS, 46–51. https://doi.org/10.1109/SDS51136.2021.00015
• Simmler, N., Sager, P., Andermatt, P., Chavarriaga, R., Schilling, F.-P., Rosenthal, M., & Stadelmann, T. (2021). A survey of un-, weakly-, and semi-supervised learning methods for noisy, missing and partial labels in industrial vision applications [Conference paper]. Proceedings of the 8th SDS, 26–31. https://doi.org/10.1109/SDS51136.2021.00012
• Amirian, M., Tuggener, L., Chavarriaga, R., Satyawan, Y. P., Schilling, F.-P., Schwenker, F., & Stadelmann, T. (2021, March). Two to trust : AutoML for safe modelling and interpretable deep learning for robustness. Postproceedings of the 1st TAILOR Workshop on Trustworthy AI at ECAI 2020. https://doi.org/10.21256/zhaw-22061
• Artificial neural networks in pattern recognition : proceedings of the 9th IAPR TC3 workshop, ANNPR 2020, Winterthur, Switzerland, September 2–4, 2020. (2020). In F.-P. Schilling & T. Stadelmann (Eds.), 9th IAPR TC 3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR′20), Winterthur, Switzerland, 2-4 September 2020. Springer. https://doi.org/10.1007/978-3-030-58309-5
• Glüge, S., Amirian, M., Flumini, D., & Stadelmann, T. (2020). How (not) to measure bias in face recognition networks [Conference paper]. In F.-P. Schilling & T. Stadelmann (Eds.), Artificial Neural Networks in Pattern Recognition. Springer. https://doi.org/10.1007/978-3-030-58309-5_10
• Roost, D., Meier, R., Toffetti Carughi, G., & Stadelmann, T. (2020, August 31). Combining reinforcement learning with supervised deep learning for neural active scene understanding. Active Vision and Perception in Human(-Robot) Collaboration Workshop at IEEE RO-MAN 2020 (AVHRC’20), Online, 31 August - 4 September 2020. https://doi.org/10.21256/zhaw-20419
• Roost, D., Meier, R., Huschauer, S., Nygren, E., Egli, A., Weiler, A., & Stadelmann, T. (2020, June 26). Improving sample efficiency and multi-agent communication in RL-based train rescheduling. Proceedings of the 7th SDS. https://doi.org/10.21256/zhaw-19978
• Tuggener, L., Amirian, M., Rombach, K., Lörwald, S., Varlet, A., Westermann, C., & Stadelmann, T. (2019). Automated machine learning in practice : state of the art and recent results [Conference paper]. 2019 6th Swiss Conference on Data Science (SDS), 31–36. https://doi.org/10.1109/SDS.2019.00-11
• Tuggener, L., Elezi, I., Schmidhuber, J., & Stadelmann, T. (2018). Deep watershed detector for music object recognition. Proceedings of the 19th International Society for Music Information Retrieval Conference. https://doi.org/10.21256/zhaw-3760
• Stadelmann, T., Glinski-Haefeli, S., Gerber, P., & Dürr, O. (2018). Capturing suprasegmental features of a voice with RNNs for improved speaker clustering [Conference paper]. Artificial Neural Networks in Pattern Recognition, 333–345. https://doi.org/10.1007/978-3-319-99978-4_26
• Stadelmann, T., Amirian, M., Arabaci, I., Arnold, M., Duivesteijn, G. F., Elezi, I., Geiger, M., Lörwald, S., Meier, B. B., Rombach, K., & Tuggener, L. (2018). Deep learning in the wild [Conference paper]. Artificial Neural Networks in Pattern Recognition, 17–38. https://doi.org/10.1007/978-3-319-99978-4_2
• Amirian, M., Schwenker, F., & Stadelmann, T. (2018). Trace and detect adversarial attacks on CNNs using feature response maps [Conference paper]. Artificial Neural Networks in Pattern Recognition, 346–358. https://doi.org/10.1007/978-3-319-99978-4_27
• Tuggener, L., Elezi, I., Schmidhuber, J., Pelillo, M., & Stadelmann, T. (2018). DeepScores : a dataset for segmentation, detection and classification of tiny objects [Conference paper]. 2018 24th International Conference on Pattern Recognition (ICPR), 1–3704. https://doi.org/10.1109/ICPR.2018.8545307
• Meier, B. B., Elezi, I., Amirian, M., Dürr, O., & Stadelmann, T. (2018). Learning neural models for end-to-end clustering [Conference paper]. Artificial Neural Networks in Pattern Recognition, 126–138. https://doi.org/10.1007/978-3-319-99978-4_10
• Hibraj, F., Vascon, S., Stadelmann, T., & Pelillo, M. (2018). Speaker clustering using dominant sets [Conference paper]. 2018 24th International Conference on Pattern Recognition (ICPR), 3549–3554. https://doi.org/10.1109/ICPR.2018.8546067
• Elezi, I., Tuggener, L., Pelillo, M., & Stadelmann, T. (2018). DeepScores and Deep Watershed Detection : current state and open issues [Conference paper]. Proceedings of the 1st International Workshop on Reading Music Systems, 13–14. https://doi.org/10.21256/zhaw-4777
• Lukic, Y. X., Vogt, C., Dürr, O., & Stadelmann, T. (2017). Learning embeddings for speaker clustering based on voice equality. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP). https://doi.org/10.1109/MLSP.2017.8168166
• Meier, B., Stadelmann, T., Stampfli, J., Arnold, M., & Cieliebak, M. (2017). Fully convolutional neural networks for newspaper article segmentation. Proceedings of the 14th IAPR International Conference on Document Analysis and Recognition (ICDAR). https://doi.org/10.21256/zhaw-1533
• Lukic, Y., Vogt, C., Dürr, O., & Stadelmann, T. (2016). Speaker identification and clustering using convolutional neural networks. 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP),. https://doi.org/10.1109/MLSP.2016.7738816
• Arnold, M., Cieliebak, M., Stadelmann, T., Stampfli, J., & Uzdilli, F. (2015). PANOPTES : automated article segmentation of newspaper pages for “Real Time Print Media Monitoring“. Proceedings of SGAICO Annual Assembly and Workshop 2015. https://doi.org/10.21256/zhaw-7739
• Stadelmann, T., Stockinger, K., Braschler, M., Cieliebak, M., Baudinot, G., Dürr, O., & Ruckstuhl, A. (2013). Applied data science in Europe : challenges for academia in keeping up with a highly demanded topic. Proceedings of the 9th European Computer Science Summit.

Other publications

• Stadelmann, T. (2025). A guide to AI : understanding the technology, applying it successfully, and shaping a positive future (G. Prabhu Siddhartha, Ed.). Global Resilience Publishing. https://doi.org/10.21256/zhaw-32180
• Segessenman, J., Stadelmann, T., Andrew, D., & Oliver, D. (2023). Assessing deep learning : a work program for the humanities in the age of artificial intelligence. SSRN. https://doi.org/10.21256/zhaw-28651
• von der Malsburg, C., Stadelmann, T., & Grewe, B. F. (2022). A theory of natural intelligence. arXiv. https://doi.org/10.48550/ARXIV.2205.00002
• Stadelmann, T., & Würsch, C. (2020). Maps for an uncertain future : teaching AI and machine learning using the ATLAS concept. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-20885
• Amirian, M., Rombach, K., Tuggener, L., Schilling, F.-P., & Stadelmann, T. (2019). Efficient deep CNNs for cross-modal automated computer vision under time and space constraints. ECML-PKDD 2019, Würzburg, Germany, 16-19 September 2019. https://doi.org/10.21256/zhaw-18357
• Stadelmann, T., & Schilling, F.-P. (2019). Deep Learning in medizinischer Diagnostik und Qualitätskontrolle. Netzwoche. https://doi.org/10.21256/zhaw-20163
• Stadelmann, T., Cieliebak, M., & Stockinger, K. (2015). Toward automatic data curation for open data. ERCIM News, 2015(100), 32–33. https://doi.org/10.21256/zhaw-3643

Oral conference contributions and abstracts

• Stadelmann, T. (2026, April 1). Lernen in komplexen Systemen : KI, Human Factors und die Zukunft der Gefahrenabwehr. Abstractband zum zweiten Symposium für Taktische Einsatz- Notfall- und Katastrophenmedizin (TENuK).
• Lanfant, B., Lack, S., Meyer, B., Abdulkadir, A., Stadelmann, T., & Schmid, D. (2024, September 17). 3D-master-based method for optimizing the cost calculation of PBF-LB/M manufactured parts. Metal Additive Manufacturing Conference (MAMC), Aachen, Germany, 17-19 September 2024.
• Stadelmann, T. (2023, January 20). KI als Chance für die angewandten Wissenschaften im Wettbewerb der Hochschulen. Bürgenstock-Konferenz der Schweizer Fachhochschulen und Pädagogischen Hochschulen, Luzern, Schweiz, 20.-21. Januar 2023. https://www.buergenstock-konferenz.ch/images/2023/19_Website_Eingabe_Stadelmann.pdf
• von der Malsburg, C., Grewe, B. F., & Stadelmann, T. (2022, September 5). Making sense of the natural environment. The Biannual Conference of the German Cognitive Science Society (KogWis), Freiburg, Germany, 5-7 September 2022. 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

• Complete list of publications, including all preprints as PDFs
• Guide to the extensive body of publioshed work
• Bibliometrics on Google Scholar

Research data

Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen; Stadelmann, Thilo, 2020. DeepScoresV2. Zenodo. Available from: https://doi.org/10.5281/zenodo.4012193

Affiliations

Current Affiliations

AlpineAI AG: Member of the Board
I am a member of the Board of Directors. AlpineAI produces a secure, open source-based AI solution (similar to ChatGPT) for high-stakes areas (e.g. public authorities, healthcare sector)., Davos Platz
08 / 2023 - today

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