Prof. Dr. Kurt Stockinger

Arbeit an der ZHAW

Tätigkeit

• Professor für Informatik
• Leitung MAS Data Science
• Co-Leitung ZHAW Datalab
• Schwerpunktleitung "Intelligent Information Systems"
• Co-Autor des Buches "Applied Data Science", Springer 2019
• Affiliiert mit Universität Zürich

Arbeits- und Forschungsschwerpunkte

• Intelligent Information Systems
• Mein Forschungsschwerpunkt liegt an der Schnittstelle von Informationssystemen, Verarbeitung natürlicher Sprache und maschinellem Lernen
• Konkretere Themen sind: Data Science, Big Data, Natural Language Query Processing, Question Answering over Knowledge Graphs, Maschinelles Lernen für Informationssysteme, Quantum Machine Learning

Lehrtätigkeit

• Datenbanken
• Information Engineering
• Quantum Computing (Quantum Machine Learning)
• Big Data for Natural Sciences (Universität Zürich)

Berufserfahrung

• Professor mit Promotionsrecht an der mathematisch-naturwissenschaftlichen Fakultät
  Universität Zürich, Schweiz
  11 / 2022 - heute
• Professor für Informatik
  Züricher Hochschule für Angewandte Wissenschaften, Schweiz
  07 / 2016 - heute
• Gastwissenschaftler
  University of Washington, Seattle, Washington, USA
  02 / 2024 - 08 / 2024
• Externer Dozent
  Universität Zürich, Schweiz
  02 / 2022 - 10 / 2022
• Dozent für Informatik
  Zürcher Hochschule für Angewandte Wissenschaften, Schweiz
  08 / 2013 - 06 / 2016
• Data Warehouse & Business Intelligence Architect
  Credit Suisse, Zürich, Schweiz
  11 / 2007 - 07 / 2013
• Computerwissenschaftler
  Lawrence Berkeley National Laboratory, Berkeley, Kalifornien, USA
  02 / 2003 - 09 / 2007
• Computerwissenschaftler
  CERN, Genf, Schweiz
  05 / 1999 - 12 / 2003
• Gastwissenschaftler
  California Institute of Technology, Pasadena, Kalifornien, USA
  03 / 2001 - 06 / 2001

Aus- und Weiterbildung

Ausbildung

• CAS Didaktik & Methodik
  Zürcher Hochschule für Angewandte Wissenschaften, Schweiz
  03 / 2014 - 05 / 2014
• Online Kurs / Machine Learning
  Stanford University, USA
  03 / 2012 - 12 / 2012
• Doktorat / Informatik
  Universität Wien, Österreich und CERN, Schweiz
  05 / 1999 - 12 / 2001
• Master / Wirtschaftsinformatik
  Universität Wien, Österreich
  10 / 1994 - 03 / 1999
• Erasmus-Austauschprogramm / Informatik
  Royal Holloway College, University of London, England
  09 / 1996 - 06 / 1997

Netzwerk

ORCID digital identifier

ORCID ID: 0000-0003-4034-4812

Social Media

• Quantum Machine Learning
• Intelligent Open Data Exploration Applied to Astrophysics and Bioinformatics
• Das ZHAW Datalab

Medienpräsenz

• Kurze Zusammenfassung der Forschungsschwerpunkte
• QuantumBasel und ZHAW School of Engineering forschen gemeinsam im Bereich Quantum Machine Learning
• ZHAW-Forschende wenden Quantencomputer praktisch an

Projekte

• AI4Flex.Data: KI-gesteuerte Cross-Engine-Optimierung paralleler Workloads (SNF) / Projektleiter:in / laufend
• QML – Quantum Machine Learning / Projektleiter:in / laufend
• KI-gestützte klinische Entscheidungsfindung in der Radiologie durch multimodale Analyse / Co-Projektleiter:in / laufend
• DataGEMS - Data Discovery Platform with Generalized Exploratory, Management, and Search Capabilities (Horizon Europe) / Projektleiter:in / laufend
• Digital Health Zurich: ein Praxislabor für patientenzentrierte klinische Innovation / Stellv. Projektleiter:in / laufend
• Reliable Multi-lingual and Cross-lingual Open Data Exploration in Natural Language / Co-Projektleiter:in / abgeschlossen
• INODE4StatBot.swiss – Anwendung neuer Algorithmen zur automatischen Übersetzung natürlicher Sprache in die Datenbankabfragesprache SQL (NL-to-SQL) / Projektleiter:in / abgeschlossen
• DECIDE – Digital Enabling of Circularity, Innovation, Development and Environment / Projektleiter:in / abgeschlossen
• DataInc – Intelligent Data Integration and Cleaning / Teammitglied / abgeschlossen
• GraphQueryML – Verwendung von maschinellem Lernen zur Optimierung von Abfragen in Graphdatenbanken (SNF/DFG) / Projektleiter:in / abgeschlossen
• INODE – Intelligent Open Data Exploration (EU Horizon 2020) / Projektleiter:in / abgeschlossen
• NQuest – Natural Language Query Exploration System / Projektleiter:in / abgeschlossen
• NatalieDB: Natural Language Interface for Databases / Projektleiter:in / abgeschlossen
• Decision Support System for Predictive Maintenance of Laser Cutting Machines / Teammitglied / abgeschlossen
• Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data / Projektleiter:in / abgeschlossen
• Large Scale Data-Driven Financial Risk Modelling / Teammitglied / abgeschlossen
• Accurate Customer Identification on Digital Channels / Projektleiter:in / abgeschlossen
• Real-time price anomaly detection for market data quality monitoring / Teammitglied / abgeschlossen
• Quantified Self – Schnittstelle zwischen Lifestyle und Medizin / Teammitglied / abgeschlossen
• Smart Alarms and Verified Events / Projektleiter:in / abgeschlossen
• Roche Business Analytics / Projektleiter:in / abgeschlossen
• Urban Water Research Data Warehouse / Projektleiter:in / abgeschlossen
• An Innovative E-Commerce Application Using Modern Machine Learning Technology / Projektleiter:in / abgeschlossen
• Big Data Query Processing / Projektleiter:in / abgeschlossen
• Placebook – An Innovative Parking Space Spot Market / Teammitglied / abgeschlossen
• iisiBox – Easy access to educational servers. / Teammitglied / abgeschlossen
• NoSQL Data Warehouse / Projektleiter:in / abgeschlossen
• SODES – Swiss Open Data Exploration System / Teammitglied / abgeschlossen

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• De Meer Pardo, F. et al. (2025) 'TransClean : finding false positives in multi-source entity matching under real-world conditions via transitive consistency', IEEE Access, 13, pp. 195856–195870. doi: 10.1109/access.2025.3632400.
• Difrancesco, S. et al. (2025) 'Collaborative design and development of a patient-centered digital health app for supportive cancer care : participatory study', JMIR Human Factors, 12(e73829). doi: 10.2196/73829.
• Frehner, R. and Stockinger, K. (2025) 'Applying quantum autoencoders for time series anomaly detection', Quantum Machine Intelligence, 7(59). doi: 10.1007/s42484-025-00285-1.
• Bischof, L. et al. (2025) 'Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching', Scientific Reports, 15(1), p. 4318. doi: 10.1038/s41598-025-88177-z.
• Kosten, C., Nooralahzadeh, F. and Stockinger, K. (2025) 'Evaluating the effectiveness of prompt engineering for knowledge graph question answering', Frontiers in Artificial Intelligence, 7(1454258). doi: 10.3389/frai.2024.1454258.
• Berg, M. et al. (2024) 'distect : automatic sample-position tracking for X-ray experiments using computer vision algorithms', Journal of Synchrotron Radiation. doi: 10.1107/S1600577524009536.
• Frehner, R. et al. (2024) 'Detecting anomalies in time series using kernel density approaches', IEEE Access, 12, pp. 33420–33439. doi: 10.1109/ACCESS.2024.3371891.
• Lehmann, C., Sulimov, P. and Stockinger, K. (2024) 'Is your learned query optimizer behaving as you expect? : a machine learning perspective', Proceedings of the VLDB Endowment, 17(7), pp. 1565–1577. doi: 10.14778/3654621.3654625.
• Smith, E. et al. (2024) 'Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines', Bioinformatics Advances, 4(1), p. vbae045. doi: 10.1093/bioadv/vbae045.
• Zhang, Y. et al. (2024) 'ScienceBenchmark : a complex real-world benchmark for evaluating natural language to SQL systems', Proceedings of the VLDB Endowment, 17(4), pp. 685–698. doi: 10.14778/3636218.3636225.
• Fankhauser, T. et al. (2023) 'Multiple query optimization using a gate-based quantum computer', IEEE Access, 11, pp. 114031–114043. doi: 10.1109/ACCESS.2023.3324253.
• Mildenberger, T. et al. (2023) 'The role of data scientists in modern enterprises : experience from data science education', SIGMOD Record, 52(2), pp. 48–52. doi: 10.21256/zhaw-27357.
• Monteiro Simoes, R. D. et al. (2023) 'Experimental evaluation of quantum machine learning algorithms', IEEE Access, 11, pp. 6197–6208. doi: 10.1109/ACCESS.2023.3236409.
• Sima, A. C. et al. (2022) 'Bio-SODA UX : enabling natural language question answering over knowledge graphs with user disambiguation', Distributed and Parallel Databases, 40(2), pp. 409–440. doi: 10.1007/s10619-022-07414-w.
• Amer-Yahia, S. et al. (2022) 'INODE : building an end-to-end data exploration system in practice', SIGMOD Record, 50(4), pp. 23–29. doi: 10.1145/3516431.3516436.
• Smith, E. et al. (2021) 'LILLIE : information extraction and database integration using linguistics and learning-based algorithms', Information Systems, 105. doi: 10.1016/j.is.2021.101938.
• Liang, S. et al. (2021) 'Querying knowledge graphs in natural language', Journal of Big Data, 8(3). doi: 10.1186/s40537-020-00383-w.
• Lehmann, C. et al. (2020) 'Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms', Journal of Big Data, 7(1). doi: 10.1186/s40537-020-00340-7.
• Sima, A.-C. et al. (2020) 'A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL', F1000Research, 8, p. 1822. doi: 10.12688/f1000research.21027.2.
• Stockinger, K. et al. (2019) 'Scalable architecture for big data financial analytics : user-defined functions vs. SQL', Journal of Big Data, 6(46). doi: 10.1186/s40537-019-0209-0.
• Sima, A.-C. et al. (2019) 'Enabling semantic queries across federated bioinformatics databases', Database: The Journal of Biological Databases and Curation, 2019(baz106). doi: 10.1093/database/baz106.
• Affolter, K., Stockinger, K. and Bernstein, A. (2019) 'A comparative survey of recent natural language interfaces for databases', The VLDB Journal. doi: 10.1007/s00778-019-00567-8.
• Stockinger, K. et al. (2017) 'ZNS : efficient query processing with ZurichNoSQL', Data & Knowledge Engineering, 2017(112), pp. 38–54. doi: 10.1016/j.datak.2017.09.004.
• Stockinger, K. and Stadelmann, T. (2014) 'Data Science für Lehre, Forschung und Praxis', HMD Praxis der Wirtschaftsinformatik, 51(4), pp. 469–479. doi: 10.1365/s40702-014-0040-1.

Bücher, peer-reviewed

• Braschler, M., Stadelmann, T. and Stockinger, K. (eds) (2019) Applied data science : lessons learned for the data-driven business. 1. Auflage. Cham: Springer. doi: 10.1007/978-3-030-11821-1.
• Meidert, U. et al. (2018) Quantified Self : Schnittstelle zwischen Lifestyle und Medizin. Zürich: vdf Hochschulverlag. doi: 10.21256/zhaw-1941.

Buchbeiträge, peer-reviewed

• Sima, A.-C. et al. (2019) 'Semantic integration and enrichment of heterogeneous biological databases', in Anisimova, M. (ed.) Evolutionary genomics : statistical and computational methods. New York: Springer, pp. 655–690. doi: 10.1007/978-1-4939-9074-0_22.
• Geiger, M. and Stockinger, K. (2019) 'Data warehousing and exploratory analysis for market monitoring', in Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 333–351. doi: 10.1007/978-3-030-11821-1_18.
• Ackermann, P. and Stockinger, K. (2019) 'Narrative visualization of open data', in Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 251–264. doi: 10.1007/978-3-030-11821-1_14.
• Breymann, W. et al. (2019) 'Large-scale data-driven financial risk assessment', in Braschler, M., Stadelmann, T., and Stockinger, K. (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 387–408. doi: 10.1007/978-3-030-11821-1_21.
• Braschler, M., Stadelmann, T. and Stockinger, K. (2019) 'Data science', in Braschler, M., Stadelmann, T., and Stockinger, K. (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 17–29. doi: 10.1007/978-3-030-11821-1_2.
• Stockinger, K., Braschler, M. and Stadelmann, T. (2019) 'Lessons learned from challenging data science case studies', in Braschler, M., Stadelmann, T., and Stockinger, K. (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 447–465. doi: 10.1007/978-3-030-11821-1_24.
• Stadelmann, T. et al. (2019) 'Data scientists', in Braschler, M., Stadelmann, T., and Stockinger, K. (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 31–45. doi: 10.1007/978-3-030-11821-1_3.
• Stadelmann, T., Braschler, M. and Stockinger, K. (2019) 'Introduction to applied data science', in Braschler, M., Stadelmann, T., and Stockinger, K. (eds) Applied data science : lessons learned for the data-driven business. Cham: Springer, pp. 3–16. doi: 10.1007/978-3-030-11821-1_1.
• Stockinger, K., Stadelmann, T. and Ruckstuhl, A. (2016) 'Data Scientist als Beruf', in Fasel, D. and Andreas, M. (eds) Big Data. Wiesbaden: Springer, pp. 59–81. doi: 10.1007/978-3-658-11589-0_4.

Schriftliche Konferenzbeiträge, peer-reviewed

• Lehmann, C., Stockinger, K. and Sulimov, P. (2026) 'Flood detection in Switzerland using Sentinel-2 satellite imagery', in 2026 IEEE Swiss Conference on Data Science and AI (SDS). IEEE, pp. 185–188. doi: 10.1109/SDS70563.2026.00033.
• Saxer, J. et al. (2026) 'Query carefully : detecting the unanswerables in text-to-SQL tasks', in Bruno, P. et al. (eds) Artificial intelligence for healthcare, and hybrid models for coupling deductive and inductive reasoning. Cham: Springer, pp. 497–509. doi: 10.1007/978-3-032-16708-8_39.
• Nooralahzadeh, F. et al. (2025) 'Multi-modal data exploration via language agents', in Inui, K. et al. (eds) Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 795–813. doi: 10.18653/v1/2025.findings-ijcnlp.47.
• Sulimov, P., Lehmann, C. and Stockinger, K. (2025) 'GenJoin : conditional generative plan-to-plan query optimizer that learns from subplan hints', in Proceedings of the ACM on Management of Data. Association for Computing Machinery, p. 247. doi: 10.1145/3749165.
• Kosten, C. et al. (2025) 'Bootstrapping text-to-SQL resources for knowledge graph question answering', in 2025 IEEE Swiss Conference on Data Science (SDS). IEEE, pp. 8–15. doi: 10.1109/SDS66131.2025.00009.
• de Meer Pardo, F. et al. (2025) 'GraLMatch : matching groups of entities with graphs and language models', in Proceedings of EDBT 2025. OpenProceedings, pp. 1–12. doi: 10.48786/edbt.2025.01.
• Fürst, J. et al. (2025) 'Evaluating the data model robustness of Text-to-SQL systems based on real user queries', in Proceedings of EDBT 2025. OpenProceedings, pp. 158–170. doi: 10.48786/edbt.2025.13.
• Sivasubramaniam, S. et al. (2024) 'SM3-Text-to-Query : synthetic multi-model medical text-to-query benchmark', in 38th Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 10-15 December 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-32078.
• Nooralahzadeh, F. et al. (2024) 'StatBot.Swiss : bilingual open data exploration in natural language', in Findings of the Association for Computational Linguistics: ACL 2024. Association for Computational Linguistics, pp. 5486–5507. doi: 10.18653/v1/2024.findings-acl.326.
• Kittelmann, F., Sulimov, P. and Stockinger, K. (2024) 'QardEst : using quantum machine learning for cardinality estimation of join queries', in 1st Workshop on Quantum Computing and Quantum-Inspired Technology for Data-Intensive Systems and Applications (Q-Data), ACM SIGMOD/PODS 2024, Santiago, Chile, 9 June 2024. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-30917.
• Kosten, C., Cudré-Mauroux, P. and Stockinger, K. (2024) 'Spider4SPARQL : a complex benchmark for evaluating knowledge graph question answering systems', in 2023 IEEE International Conference on Big Data (BigData). IEEE. doi: 10.1109/BigData59044.2023.10386182.
• Lehmann, C. et al. (2022) 'Building natural language interfaces for databases in practice', in Proceedings of the 34th SSDBM. Association for Computing Machinery. doi: 10.1145/3538712.3538744.
• von Däniken, P. et al. (2022) 'Improving NL-to-Query systems through re-ranking of semantic hypothesis', in Abbas, M. and Freihat, A. A. (eds) Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022). Association for Computational Linguistics, pp. 57–67. doi: 10.21256/zhaw-26147.
• Holzer, S. and Stockinger, K. (2022) 'Detecting errors in databases with bidirectional recurrent neural networks', in Proceedings of EDBT 2022. OpenProceedings, pp. 364–367. doi: 10.48786/edbt.2022.22.
• Klingler, Y. et al. (2022) 'Evaluation of algorithms for interaction-sparse recommendations : neural networks don't always win', in Proceedings of EDBT 2022. OpenProceedings, pp. 475–486. doi: 10.48786/edbt.2022.42.
• Sima, A. C. et al. (2021) 'Bio-SODA : enabling natural language question answering over knowledge graphs without training data', in Proceedings of the 33rd SSDBM. Association for Computing Machinery, pp. 61–72. doi: 10.1145/3468791.3469119.
• Brunner, U. and Stockinger, K. (2021) 'ValueNet : a natural language-to-SQL system that learns from database information', in Proceedings of the 37th ICDE. IEEE, pp. 2177–2182. doi: 10.1109/ICDE51399.2021.00220.
• Deriu, J. M. et al. (2020) 'A methodology for creating question answering corpora using inverse data annotation', in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, pp. 897–911. doi: 10.18653/v1/2020.acl-main.84.
• Eslahi, Y. et al. (2020) 'Annotating web tables through knowledge bases : a context-based approach (Best Paper Award)', in Proceedings of the 7th SDS. IEEE. doi: 10.1109/SDS49233.2020.00013.
• Nadig, S., Braschler, M. and Stockinger, K. (2020) 'Database search vs. information retrieval : a novel method for studying natural language querying of semi-structured data', in Proceedings of the 12th LREC. European Language Resources Association. doi: 10.21256/zhaw-20042.
• Brunner, U. and Stockinger, K. (2020) 'Entity matching with transformer architectures - a step forward in data integration', in Proceedings of EDBT 2020. OpenProceedings, pp. 463–473. doi: 10.5441/002/edbt.2020.58.
• Mendes de Farias, T., Stockinger, K. and Dessimoz, C. (2019) 'VoIDext : vocabulary and patterns for enhancing interoperable datasets with virtual links', in OTM 2019 Conference Proceedings. Cham: Springer, pp. 607–625. doi: 10.1007/978-3-030-33246-4_38.
• Brunner, U. and Stockinger, K. (2019) 'Entity matching on unstructured data : an active learning approach', in Proceedings of the 6th SDS. IEEE, pp. 97–102. doi: 10.1109/SDS.2019.00006.
• Sima, A.-C. et al. (2018) 'A hybrid approach for alarm verification using stream processing, machine learning and text analytics', in Proceedings of the 21st International Conference on Extending Database Technology. Association for Computing Machinery. doi: 10.21256/zhaw-3487.
• Stockinger, K. et al. (2018) 'Large-scale data-driven financial risk modeling using big data technology', in 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT). IEEE, pp. 206–207. doi: 10.1109/BDCAT.2018.00033.
• Graf, H. D. et al. (2017) 'Four different ways to build a chatbot about movies', in SwissText 2017: 2nd Swiss Text Analytics Conference, Winterthur, 9. Juni 2017.
• Stadelmann, T. et al. (2013) 'Applied data science in Europe : challenges for academia in keeping up with a highly demanded topic', in Proceedings of the 9th European Computer Science Summit.

Weitere Publikationen

• Chen, Y. et al. (2024) Digital public infrastructure for environmental sustainability. United Nations Environment Programme. doi: 10.21256/zhaw-30874.
• Stampfli, J. and Stockinger, K. (2016) 'Applied data science : using machine learning for alarm verification : a novel alarm verification service applying various machine learning algorithms can identify false alarms', ERCIM News, 107, p. 10. doi: 10.21256/zhaw-3785.
• Stockinger, K., van Lingen, F. and Valente, M. (2015) 'Big data analytics in a connected world', Business Intelligence Journal, 20(2). Available at: https://tdwi.org/research/2015/06/business-intelligence-journal-vol-20-no-2.aspx.
• Stadelmann, T., Cieliebak, M. and Stockinger, K. (2015) 'Toward automatic data curation for open data', ERCIM News, 2015(100), pp. 32–33. doi: 10.21256/zhaw-3643.
• Imhof, M. et al. (2014) Evaluation of query processing with impala for mixed workloads. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. doi: 10.21256/zhaw-3786.
• Stockinger, K. (2013) 'Data Scientists : die neuen Helden des 21. Jahrhunderts?', Tages-Anzeiger, p. 29.

Mündliche Konferenzbeiträge und Abstracts

• Fankhauser, T. et al. (2021) 'Implementing database and machine learning algorithms on publicly available quantum computers', in Talk at Zurich University, Zurich, Switzerland, 4 May 2021.
• Weber, R. et al. (2020) 'Quantum databases and quantum machine learning : how far can we go on a publicly available quantum computer?', in Datalab Seminar ZHAW, Winterthur, 9. September 2020. Available at: https://www.zhaw.ch/de/forschung/departementsuebergreifende-kooperationen/datalab/datalab-seminar/.
• Breymann, W. and Stockinger, K. (2014) 'Data-driven financial system modeling (DatFisMo)', in PWC Swiss Data Week 2014, Zurich, 5-9 May 2014.

Publikationen vor Tätigkeit an der ZHAW

Siehe Publikationen unter Google Scholar

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