Prof. Dr. Thilo Stadelmann

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

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

Arbeit an der ZHAW

Tätigkeit

Arbeits- und Forschungsschwerpunkte

Künstliche Intelligenz und maschinelles Lernen sowie deren gesellschaftliche Wirkungen
Forschungsinteressen: Sensorische Mustererkennung mittels Deep Learning; Dokumentenerkennung und Multimedia-Analyse (z. B. für Industrie und Medizin); Entwicklung biologisch inspirierter neuronaler Systeme und Weltmodelle ("world models"); gesellschaftliche Auswirkungen von KI und menschenzentriertes KI-Design ("pro-human AI")
KI-Aufgaben in der Praxis beginnen häufig mit der Erkennung von Mustern in Sensordaten (wie Ähnlichkeiten und Anomalien in Bild-, Video-, Zeitreihen- oder Audiodaten) und erstrecken sich bis hin zur Interpretation dieser Muster (was zu Aktionen wie Klassifikation, Segmentierung, Vorhersage oder Entscheidungsfindung führt). Die MPC-Gruppe umspannt diesen Bogen von der Wahrnehmung zur Kognition, ist in der Mustererkennung verwurzelt und fokussiert sich auf Methoden tiefer neuronaler Netze. Die von uns untersuchten Aufgaben weisen unterschiedliche Lernziele auf (z. B. Detection, Klassifikation, Clustering, Segmentierung, Novelty Detection, Control) sowie entsprechende praktische Anwendungsfälle (z. B. Predictive Maintenance für industrielle Anlagen, Speaker Recognition für Multimedia-Indexierung, Dokumentenanalyse im Bauwesen, Computer Vision für industrielle Qualitätskontrolle, automatisiertes maschinelles Lernen in den allgemeinen Datenwissenschaften, Deep Reinforcement Learning für Gebäudesteuerung, Videoanalyse für automatische Medienproduktion, Face Recognition für biometrische Zugangskontrollen, Human-AI Co-Learning für sinnvolle Mensch-KI-Kollaboration in sicherheitskritischen Szenarien), die ihrerseits verschiedene Aspekte des Lernprozesses beleuchten. Wir nutzen diese Erfahrung, um zunehmend allgemeine KI-Systeme für die Praxis zu entwickeln, die auf neuronalen Architekturen basieren. Grundlegende Herausforderungen liegen dabei in der Robustheit der Systeme sowie in ihrer Sample- und Label-Effizienz und werden häufig mit Transfer Learning und Domain Adaptation angegangen. Darüber hinaus lassen wir uns vom biologischen Lernen inspirieren, um an KI-Methoden der nächsten Generation mit World Models zu arbeiten, und engagieren uns aktiv an der Schnittstelle von Technologie und Gesellschaft – mit dem Ziel, zu einer lebenswerten Zukunft mit pro-humaner KI beizutragen.

Lehrtätigkeit

Lehrtätigkeit in der Weiterbildung

Berufserfahrung

Gründer, Verwaltungsrat
AlpineAI AG
07 / 2023 - heute
Redner
Premium Speakers Agency
05 / 2023 - heute
Professor für AI/ML, Leiter ZHAW CAI, Leiter MPC Forschungsgruppe
ZHAW School of Engineering
04 / 2021 - heute
Co-Gründer und Vorstand, Leiter 2013-2018
ZHAW Datalab
05 / 2013 - heute
Co-Gründer, Vorstand, Managing Director 2017-2018
Data innovation alliance
01 / 2016 - 11 / 2023
Wissenschaftlicher Leiter
ZHAW digital
01 / 2019 - 03 / 2021
Stv. Leiter Forschungsschwerpunkt Information Engineering, Professor für Informatik ab 2018
ZHAW Institut für angewandte Informationstechnologie
06 / 2015 - 03 / 2021
Dozent für Information Engineering
ZHAW Institut für angewandte Informationstechnologie
02 / 2013 - 06 / 2018
Software-Architekt & Projektleiter, Teamleiter Smarte Software (ab 2011), Leiter IT (ab 2012)
TWT GmbH Science & Innovation
07 / 2010 - 01 / 2013
Wissenschaftlicher Mitarbeiter im Bereich Audio- und Videomining
Philipps-Universität Marburg
09 / 2004 - 06 / 2010
Diverse Nebentätigkeiten in Software-Entwicklung und Data Mining
diverse
02 / 1998 - 06 / 2010

Aus- und Weiterbildung

Ausbildung

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

Weiterbildung

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

Netzwerk

Mitglied in Netzwerken

ORCID digital identifier

ORCID ID: 0000-0002-3784-0420

Auszeichnungen

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
Betreuer mehrerer prämierter Abschlussarbeiten (u.a. ZHAW SGD Award, Siemens Excellence Award, Lab Sciences Award, Dr. Waldemar Jucker Award)
verschiedene
01 / 2024
Most Cited Paper Award für "Automated Machine Learning in Practice"
IEEE 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
Lehrpreis "Best Teaching - Best Practices"
ZHAW
09 / 2019

Social Media

Medienpräsenz

Projekte

Ausgewählte Publikationen

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

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, 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

Bücher, 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

Buchbeiträge, 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

Schriftliche Konferenzbeiträge, 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). Topology-aware node dropping augmentation for graph classification [Conference paper]. 2026 6th International Conference on Neural Networks, Information and Communication Engineering (NNICE), 589–594. https://doi.org/10.1109/NNICE68970.2026.11465527
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.

Weitere Publikationen

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

Mündliche Konferenzbeiträge und 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

Publikationen vor Tätigkeit an der ZHAW

Übrige Publikationen

Forschungsdaten

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

Interessenbindungen

Aktuelle Interessenbindungen

AlpineAI AG: Verwaltungsrat
Ich bin Mitglied des Verwaltungsrates. AlpineAI stellt eine sichere, auf Open Source basierende KI-Lösung (ähnlich ChatGPT) für High-Stakes Bereiche (etwa Behörden, Gesundheitssektor) her., Davos Platz
08 / 2023 - heute

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