Prof. Dr. Kurt Stockinger

Work at ZHAW

Position

• Professor of Computer Science
• Head MAS Data Science
• Co-Head ZHAW Datalab
• Head of Research Group "Intelligent Information Systems"
• Co-author of the book „Applied Data Science“, Springer 2019
• Affiliated with University of Zurich

Focus

• Intelligent Information Systems
• My main research focus is at the intersection of information systems, natural language processing and machine learning.
• More specific topics are: Data Science, Big Data, Natural Language Query Processing, Question Answering over Knowledge Graphs, Machine Learning for Information Systems, Quantum Machine Learning

Teaching

• Databases
• Information Engineering
• Quantum Computing (Quantum Machine Learning)
• Big Data for Natural Sciences (University of Zurich)

Professional development teaching

• DAS Data Science
• MAS Data Science

Experience

• Professor with Right to Confer a Ph.D. at the Faculty of Science
  University of Zurich, Switzerland
  11 / 2022 - today
• Professor of Computer Science
  Zurich University of Applied Sciences, Switzerland
  07 / 2016 - today
• Visiting Scholar
  University of Washington, Seattle, Washington, USA
  02 / 2024 - 08 / 2024
• External Lecturer
  University of Zurich, Switzerland
  02 / 2022 - 10 / 2022
• Associate Professor of Computer Science
  Zurich University of Applied Sciences, Switzerland
  08 / 2013 - 06 / 2016
• Data Warehouse & Business Intelligence Architect
  Credit Suisse, Zurich, Switzerland
  11 / 2007 - 07 / 2013
• Computer Scientist
  Lawrence Berkeley National Laboratory, Berkeley, California, USA
  02 / 2003 - 09 / 2007
• Computer Scientist
  CERN, Geneva, Switzerland
  05 / 1999 - 12 / 2003
• Visiting Researcher
  California Institute of Technology, Pasadena, California, USA
  03 / 2001 - 06 / 2001

Education and Continuing education

Education

• CAS Didaktik & Methodik
  Zurich University of Applied Sciences, Switzerland
  03 / 2014 - 05 / 2014
• Online course / Machine Learning
  Stanford University, USA
  03 / 2012 - 12 / 2012
• Ph.D. / Computer Science
  University of Vienna, Austria and CERN, Switzerland
  05 / 1999 - 12 / 2001
• Master / Computer Science and Business Administration
  University of Vienna, Austria
  10 / 1994 - 03 / 1999
• Erasmus Exchange Program / Computer Science
  Royal Holloway College, University of London, England
  09 / 1996 - 06 / 1997

Network

ORCID digital identifier

ORCID ID: 0000-0003-4034-4812

Social media

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

Media presence

• Brief summary of research topics
• QuantumBasel and ZHAW School of Engineering join Forces for Quantum Machine Learning Research
• ZHAW researchers apply quantum computers in practice

Projects

• AI4Flex.Data: AI-Driven Cross-Engine Optimization of Concurrent Workloads (SNF) / Project leader / ongoing
• QML – Quantum Machine Learning / Project leader / ongoing
• Radiology AI-Driven Clinical Decision-Making with Multi-Modal Exploration / Co-project leader / ongoing
• DataGEMS - Data Discovery Platform with Generalized Exploratory, Management, and Search Capabilities (Horizon Europe) / Project leader / ongoing
• Digital Health Zurich: a practice lab for patient-centred clinical innovation / Deputy project leader / ongoing
• Reliable Multi-lingual and Cross-lingual Open Data Exploration in Natural Language / Co-project leader / completed
• INODE4StatBot.swiss – Application of new algorithms for automatic natural language translation into database query language SQL (NL-to-SQL) / Project leader / completed
• DECIDE – Digital Enabling of Circularity, Innovation, Development and Environment / Project leader / completed
• DataInc – Intelligent Data Integration and Cleaning / Team member / completed
• GraphQueryML – Using Machine Learning to Optimize Queries in Graph Databases (SNSF/DFG) / Project leader / completed
• INODE – Intelligent Open Data Exploration (EU Horizon 2020) / Project leader / completed
• NQuest – Natural Language Query Exploration System / Project leader / completed
• NatalieDB: Natural Language Interface for Databases / Project leader / completed
• Decision Support System for Predictive Maintenance of Laser Cutting Machines / Team member / completed
• Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data / Project leader / completed
• Large Scale Data-Driven Financial Risk Modelling / Team member / completed
• Accurate Customer Identification on Digital Channels / Project leader / completed
• Real-time price anomaly detection for market data quality monitoring / Team member / completed
• Quantified self – halfway between lifestyle and medicine / Team member / completed
• Smart Alarms and Verified Events / Project leader / completed
• Roche Business Analytics / Project leader / completed
• Urban Water Research Data Warehouse / Project leader / completed
• An Innovative E-Commerce Application Using Modern Machine Learning Technology / Project leader / completed
• Big Data Query Processing / Project leader / completed
• NoSQL Data Warehouse / Project leader / completed
• SODES – Swiss Open Data Exploration System / Team member / completed

Publications

Articles in scientific journal, 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.
• 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.
• 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.
• 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.
• 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.
• 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. (2019) 'Enabling semantic queries across federated bioinformatics databases', Database: The Journal of Biological Databases and Curation, 2019(baz106). doi: 10.1093/database/baz106.
• 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.
• 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.

Books, 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.

Book chapters, peer-reviewed

• 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.
• 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.
• 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.
• 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.
• 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.
• 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., 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.
• 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.

Written conference contributions, peer-reviewed

• Lehmann, C., Stockinger, K. and Sulimov, P. (2026) 'Flood detection in Switzerland using Sentinel-2 satellite imagery', in 13th IEEE Swiss Conference on Data Science and AI (SDS), Zurich, Switzerland, 6-7 May 2026. IEEE. doi: 10.21256/zhaw-36610.
• 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.
• 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.
• 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.
• 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.

Other publications

• 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.

Oral conference contributions and 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.

Publications before appointment at the ZHAW

See publications at Google Scholar

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