Thilo Stadelmann

About

Thilo Stadelmann is professor of artificial intelligence and machine learning at the ZHAW School of Engineering in Winterthur, director of the ZHAW Centre for Artificial Intelligence and head of its Computer Vision, Perception and Cognition Group. A computer scientist by training, he received his doctor of science degree from Marburg University in 2010, where he worked on multimedia analysis and voice recognition. Thilo worked in engineering and leadership roles in the automotive industry for 3 years prior to switching back to academia. His current research focuses on robust deep learning to solve diverse pattern recognition tasks such as biometrics, document analysis or computer vision for industrial and medical applications. He is fellow of the European Centre for Living Technology in Venice, Italy, co-founder and board member of Switzerland’s largest innovation network, the data innovation alliance, and of the ZHAW Datalab, one of Europe’s first dedicated research hubs for data science. His book "Applied Data Science - Lessons Learned for the Data-driven Business" was published 2019 by Springer, a pre-print on "A Theory of Natural Intelligence" that informs a future roadmap for machine learning research was released recently.

Employment

• Zurich University of Applied Sciences Prof. AI/ML, Director of Centre for AI
  2013 - Present

Education

• Philipp University of Marburg Dr. rer. nat (equivalent to PhD)
  2004 - 2010
• Technische Hochschule Mittelhessen Dipl. Inform. (FH) (equivalent to MSc)
  2000 - 2004

Projects & Funding

Projects & funding information is unavailable.

Publications (98)

• Multi-view graph pooling via dominant sets for graph classification
  Pattern Recognition 2026 DOI: 10.1016/j.patcog.2025.112786
  +Save
• Debate: Evidence-based AI risk assessment for public policy
  Public Money and Management 2026 DOI: 10.1080/09540962.2025.2541304
  +Save
• A Comprehensive Survey of Agents for Computer Use: Foundations, Challenges, and Future Directions
  Arxiv 2025 DOI: 10.48550/arXiv.2501.16150
  +Save
• A document is worth a structured record: Principled inductive bias design for document recognition
  Arxiv 2025 DOI: 10.48550/arXiv.2507.08458
  +Save
• Community-Hop: Enhancing Node Classification Through Community Preference
  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2025 DOI: 10.1007/978-3-031-80507-3_3
  +Save
• Deep Retrieval at CheckThat! 2025: Identifying Scientific Papers from Implicit Social Media Mentions via Hybrid Retrieval and Re-Ranking
  Ceur Workshop Proceedings 2025
  +Save
• Hounsfield Unit Ranges as Inductive Bias for Intra-Clinical Learning of Data-Efficient CT Segmentation Models
  Proceedings Swiss Conference on Data Science Sds 2025 DOI: 10.1109/SDS66131.2025.00008
  +Save
• Human-AI interaction in safety-critical network infrastructures
  Iscience 2025 DOI: 10.1016/j.isci.2025.113400
  +Save
• Learning Actionable World Models for Industrial Process Control
  Proceedings Swiss Conference on Data Science Sds 2025 DOI: 10.1109/SDS66131.2025.00022
  +Save
• THE COOPERATIVE NETWORK ARCHITECTURE: LEARNING STRUCTURED NETWORKS AS REPRESENTATION OF SENSORY PATTERNS
  Arxiv 2025 DOI: 10.48550/arXiv.2407.05650
  +Save