Udo Bub

Employment

• Eötvös Loránd University Research Professor (full)
  2016 - Present
• EIT ICT Labs Germany GmbH CEO
  2012 - 2017
• Deutsche Telekom AG Vice President Innovation Development
  2004 - 2011
• Detecon International GmbH Managing Consultant
  1998 - 2004
• Siemens AG Research Engineer
  1995 - 1998
• Carnegie Mellon University Visiting Scientist
  1993 - 1995

Education

• Eötvös Loránd University Dr. habil. (Habilitation)
  2015 - 2017
• Harvard University Executive Education
  2005 - 2010
• Technical University Munich Dr.-Ing. (PhD)
  1995 - 1999
• Technical University Munich Dipl.-Ing. (M.Sc.)
  1989 - 1994

Projects & Funding

Projects & funding information is unavailable.

Publications (14)

• A Business Blueprint for Data Monetization
  2027 DOI: 10.1007/978-3-032-30507-7_5
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• A process model to facilitate AI-based sensor manufacturing optimization
  Information Systems and e-Business Management 2026 DOI: 10.1007/s10257-026-00724-x
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• Data Catalogs in Data Mesh and Data Space Implementations
  2026 DOI: 10.1007/978-3-032-27096-2_20
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• Enhancing learning in business development education through a method for engineering of digital innovation
  Cogent Education 2025 DOI: 10.1080/2331186X.2024.2444816
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• Data Mesh and Data Space: A Comparative Analysis with a Focus on Governance
  2025 DOI: 10.1007/978-3-031-94263-1_10
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• Towards a Data Monetization Maturity Model
  2025 DOI: 10.1007/978-3-031-97317-8_19
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• Unsupervised Representation Learning and Explainable Clustering for Wafer Map Pattern Analysis
  IEEE Transactions on Semiconductor Manufacturing 2025 DOI: 10.1109/TSM.2025.3579031
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• Smart calibration and monitoring: leveraging artificial intelligence to improve MEMS-based inertial sensor calibration
  Complex & Intelligent Systems 2024 DOI: 10.1007/s40747-024-01531-y
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• Artificial Intelligence Applications for MEMS-Based Sensors and Manufacturing Process Optimization
  Telecom 2023 DOI: 10.3390/telecom4010011
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• A Design Science Research Approach Towards Knowledge Discovery and Predictive Maintenance of MEMS Inertial Sensors Using Machine Learning
  2023 DOI: 10.1007/978-3-031-41774-0_25
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