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)

Lehrtätigkeit in der Weiterbildung

• DAS Data Science
• MAS Data Science

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 / Projektleiter:in / Start bevorstehend
• 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., Hadji Misheva, B., Braschler, M., & Stockinger, K. (2025). TransClean : finding false positives in multi-source entity matching under real-world conditions via transitive consistency. IEEE Access, 13, 195856–195870. https://doi.org/10.1109/access.2025.3632400
• Difrancesco, S., Bauert, M. M., Lehmann, C., Häsler, S., Zhang, Y., Hirsch, S., Ackermann, P., Stockinger, K., Reif, M., Mathieu, S., Götz, A., Wicki, A., Krauthammer, M., & Witt, C. M. (2025). Collaborative design and development of a patient-centered digital health app for supportive cancer care : participatory study. JMIR Human Factors, 12(e73829). https://doi.org/10.2196/73829
• Frehner, R., & Stockinger, K. (2025). Applying quantum autoencoders for time series anomaly detection. Quantum Machine Intelligence, 7(59). https://doi.org/10.1007/s42484-025-00285-1
• Bischof, L., Teodoropol, S., Füchslin, R. M., & Stockinger, K. (2025). Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching. Scientific Reports, 15(1), 4318. https://doi.org/10.1038/s41598-025-88177-z
• Kosten, C., Nooralahzadeh, F., & Stockinger, K. (2025). Evaluating the effectiveness of prompt engineering for knowledge graph question answering. Frontiers in Artificial Intelligence, 7(1454258). https://doi.org/10.3389/frai.2024.1454258
• Berg, M., Furrer, D., Thominet, V., Wang, X., Zeugin, S., Grabner, H., Stockinger, K., & Piamonteze, C. (2024). distect : automatic sample-position tracking for X-ray experiments using computer vision algorithms. Journal of Synchrotron Radiation. https://doi.org/10.1107/S1600577524009536
• Zhang, Y., Deriu, J. M., Katsogiannis-Meimarakis, G., Kosten, C., Koutrika, G., & Stockinger, K. (2024). ScienceBenchmark : a complex real-world benchmark for evaluating natural language to SQL systems. Proceedings of the VLDB Endowment, 17(4), 685–698. https://doi.org/10.14778/3636218.3636225
• Lehmann, C., Sulimov, P., & Stockinger, K. (2024). Is your learned query optimizer behaving as you expect? : a machine learning perspective. Proceedings of the VLDB Endowment, 17(7), 1565–1577. https://doi.org/10.14778/3654621.3654625
• Frehner, R., Wu, K., Sim, A., Kim, J., & Stockinger, K. (2024). Detecting anomalies in time series using kernel density approaches. IEEE Access, 12, 33420–33439. https://doi.org/10.1109/ACCESS.2024.3371891
• Smith, E., Paloots, R., Giagkos, D., Baudis, M., & Stockinger, K. (2024). Data-driven information extraction and enrichment of molecular profiling data for cancer cell lines. Bioinformatics Advances, 4(1), vbae045. https://doi.org/10.1093/bioadv/vbae045
• Fankhauser, T., Solèr, M. E., Füchslin, R. M., & Stockinger, K. (2023). Multiple query optimization using a gate-based quantum computer. IEEE Access, 11, 114031–114043. https://doi.org/10.1109/ACCESS.2023.3324253
• Mildenberger, T., Braschler, M., Ruckstuhl, A., Vorburger, R., & Stockinger, K. (2023). The role of data scientists in modern enterprises : experience from data science education. SIGMOD Record, 52(2), 48–52. https://doi.org/10.21256/zhaw-27357
• Monteiro Simoes, R. D., Huber, P., Meier, N., Smailov, N., Füchslin, R. M., & Stockinger, K. (2023). Experimental evaluation of quantum machine learning algorithms. IEEE Access, 11, 6197–6208. https://doi.org/10.1109/ACCESS.2023.3236409
• Sima, A. C., Mendes de Farias, T., Anisimova, M., Dessimoz, C., Robinson-Rechavi, M., Zbinden, E., & Stockinger, K. (2022). Bio-SODA UX : enabling natural language question answering over knowledge graphs with user disambiguation. Distributed and Parallel Databases, 40(2), 409–440. https://doi.org/10.1007/s10619-022-07414-w
• Amer-Yahia, S., Koutrika, G., Braschler, M., Calvanese, D., Lanti, D., Lücke-Tieke, H., Mosca, A., Mendes de Farias, T., Papadopoulos, D., Patil, Y., Rull, G., Smith, E., Skoutas, D., Subramanian, S., & Stockinger, K. (2022). INODE : building an end-to-end data exploration system in practice. SIGMOD Record, 50(4), 23–29. https://doi.org/10.1145/3516431.3516436
• Smith, E., Papadopoulos, D., Braschler, M., & Stockinger, K. (2021). LILLIE : information extraction and database integration using linguistics and learning-based algorithms. Information Systems, 105. https://doi.org/10.1016/j.is.2021.101938
• Liang, S., Stockinger, K., de Farias, T. M., Anisimova, M., & Gil, M. (2021). Querying knowledge graphs in natural language. Journal of Big Data, 8(3). https://doi.org/10.1186/s40537-020-00383-w
• Sima, A.-C., Dessimoz, C., Stockinger, K., Zahn-Zabal, M., & Mendes de Farias, T. (2020). A hands-on introduction to querying evolutionary relationships across multiple data sources using SPARQL. F1000Research, 8, 1822. https://doi.org/10.12688/f1000research.21027.2
• Lehmann, C., Goren Huber, L., Horisberger, T., Scheiba, G., Sima, A.-C., & Stockinger, K. (2020). Big data architecture for intelligent maintenance : a focus on query processing and machine learning algorithms. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00340-7
• Stockinger, K., Bundi, N. A., Heitz, J., & Breymann, W. (2019). Scalable architecture for big data financial analytics : user-defined functions vs. SQL. Journal of Big Data, 6(46). https://doi.org/10.1186/s40537-019-0209-0
• Sima, A.-C., Mendes de Farias, T., Zbinden, E., Anisimova, M., Gil, M., Stockinger, H., Stockinger, K., Robinson-Rechavi, M., & Dessimoz, C. (2019). Enabling semantic queries across federated bioinformatics databases. Database: The Journal of Biological Databases and Curation, 2019(baz106). https://doi.org/10.1093/database/baz106
• Affolter, K., Stockinger, K., & Bernstein, A. (2019). A comparative survey of recent natural language interfaces for databases. The VLDB Journal. https://doi.org/10.1007/s00778-019-00567-8
• Stockinger, K., Bödi, R., Heitz, J., & Weinmann, T. O. (2017). ZNS : efficient query processing with ZurichNoSQL. Data & Knowledge Engineering, 2017(112), 38–54. https://doi.org/10.1016/j.datak.2017.09.004
• 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

• 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
• Meidert, U., Scheermesser, M., Prieur, Y., Hegyi, S., Stockinger, K., Eyyi, G., Evers-Wölk, M., Jacob, M., Oertel, B., & Becker, H. K. (2018). Quantified Self : Schnittstelle zwischen Lifestyle und Medizin. vdf Hochschulverlag. https://doi.org/10.21256/zhaw-1941

Buchbeiträge, peer-reviewed

• Ackermann, P., & Stockinger, K. (2019). Narrative visualization of open data. In Applied data science : lessons learned for the data-driven business (pp. 251–264). Springer. https://doi.org/10.1007/978-3-030-11821-1_14
• Geiger, M., & Stockinger, K. (2019). Data warehousing and exploratory analysis for market monitoring. In Applied data science : lessons learned for the data-driven business (pp. 333–351). Springer. https://doi.org/10.1007/978-3-030-11821-1_18
• Sima, A.-C., Stockinger, K., de Farias, T. M., & Gil, M. (2019). Semantic integration and enrichment of heterogeneous biological databases. In M. Anisimova (Ed.), Evolutionary genomics : statistical and computational methods (pp. 655–690). Springer. https://doi.org/10.1007/978-1-4939-9074-0_22
• Breymann, W., Bundi, N., Heitz, J., Micheler, J., & Stockinger, K. (2019). Large-scale data-driven financial risk assessment. In M. Braschler, T. Stadelmann, & K. Stockinger (Eds.), Applied data science : lessons learned for the data-driven business (pp. 387–408). Springer. https://doi.org/10.1007/978-3-030-11821-1_21
• 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., 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
• 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
• 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

• Nooralahzadeh, F., Zhang, Y., Fürst, J., & Stockinger, K. (2025). Multi-modal data exploration via language agents [Conference paper]. In K. Inui, S. Sakti, H. Wang, D. F. Wong, P. Bhattacharyya, B. Banerjee, A. Ekbal, T. Chakraborty, & D. P. Singh (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 (pp. 795–813). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-ijcnlp.47
• Sulimov, P., Lehmann, C., & Stockinger, K. (2025). GenJoin : conditional generative plan-to-plan query optimizer that learns from subplan hints [Conference paper]. Proceedings of the ACM on Management of Data, 3(4), 247. https://doi.org/10.1145/3749165
• Kosten, C., Davide, L., Cudré-Mauroux, P., & Stockinger, K. (2025). Bootstrapping text-to-SQL resources for knowledge graph question answering [Conference paper]. 2025 IEEE Swiss Conference on Data Science (SDS), 8–15. https://doi.org/10.1109/SDS66131.2025.00009
• de Meer Pardo, F., Lehmann, C., Gehrig, D., Nagy, A., Nicoli, S., Branka Hadji, M., Braschler, M., & Stockinger, K. (2025). GraLMatch : matching groups of entities with graphs and language models [Conference paper]. Proceedings of EDBT 2025, 1–12. https://doi.org/10.48786/edbt.2025.01
• Fürst, J., Kosten, C., Nooralahzadeh, F., Zhang, Y., & Stockinger, K. (2025). Evaluating the data model robustness of Text-to-SQL systems based on real user queries [Conference paper]. Proceedings of EDBT 2025, 158–170. https://doi.org/10.48786/edbt.2025.13
• Sivasubramaniam, S., Osei-Akoto, C., Zhang, Y., Stockinger, K., & Fürst, J. (2024, December 12). SM3-Text-to-Query : synthetic multi-model medical text-to-query benchmark. 38th Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada, 10-15 December 2024. https://doi.org/10.21256/zhaw-32078
• Nooralahzadeh, F., Zhang, Y., Smith, E., Maennel, S., Matthey-Doret, C., Raphaël, d. F., & Stockinger, K. (2024). StatBot.Swiss : bilingual open data exploration in natural language [Conference paper]. Findings of the Association for Computational Linguistics: ACL 2024, 5486–5507. https://doi.org/10.18653/v1/2024.findings-acl.326
• Kittelmann, F., Sulimov, P., & Stockinger, K. (2024, June). QardEst : using quantum machine learning for cardinality estimation of join queries. 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. https://doi.org/10.21256/zhaw-30917
• Kosten, C., Cudré-Mauroux, P., & Stockinger, K. (2024, January 22). Spider4SPARQL : a complex benchmark for evaluating knowledge graph question answering systems. 2023 IEEE International Conference on Big Data (BigData). https://doi.org/10.1109/BigData59044.2023.10386182
• Lehmann, C., Gehrig, D., Holdener, S., Saladin, C., Monteiro, J. P., & Stockinger, K. (2022). Building natural language interfaces for databases in practice. Proceedings of the 34th SSDBM. https://doi.org/10.1145/3538712.3538744
• von Däniken, P., Deriu, J. M., Agirre, E., Brunner, U., Cieliebak, M., & Stockinger, K. (2022). Improving NL-to-Query systems through re-ranking of semantic hypothesis [Conference paper]. In M. Abbas & A. A. Freihat (Eds.), Proceedings of the 5th International Conference on Natural Language and Speech Processing (ICNLSP 2022) (pp. 57–67). Association for Computational Linguistics. https://doi.org/10.21256/zhaw-26147
• Klingler, Y., Lehmann, C., Monteiro, J. P., Saladin, C., Bernstein, A., & Stockinger, K. (2022). Evaluation of algorithms for interaction-sparse recommendations : neural networks don’t always win [Conference paper]. Proceedings of EDBT 2022, 475–486. https://doi.org/10.48786/edbt.2022.42
• Holzer, S., & Stockinger, K. (2022). Detecting errors in databases with bidirectional recurrent neural networks [Conference paper]. Proceedings of EDBT 2022, 364–367. https://doi.org/10.48786/edbt.2022.22
• Sima, A. C., Mendes de Farias, T., Anisimova, M., Dessimoz, C., Robinson-Rechavi, M., Zbinden, E., & Stockinger, K. (2021). Bio-SODA : enabling natural language question answering over knowledge graphs without training data [Conference paper]. Proceedings of the 33rd SSDBM, 61–72. https://doi.org/10.1145/3468791.3469119
• Brunner, U., & Stockinger, K. (2021). ValueNet : a natural language-to-SQL system that learns from database information [Conference paper]. Proceedings of the 37th ICDE, 2177–2182. https://doi.org/10.1109/ICDE51399.2021.00220
• Deriu, J. M., Mlynchyk, K., Schläpfer, P., Rodrigo, A., von Grünigen, D., Kaiser, N., Stockinger, K., Agirre, E., & Cieliebak, M. (2020). A methodology for creating question answering corpora using inverse data annotation [Conference paper]. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 897–911. https://doi.org/10.18653/v1/2020.acl-main.84
• Eslahi, Y., Stockinger, K., Bhardwaj, A., Cudré-Mauroux, P., & Rosso, P. (2020, June). Annotating web tables through knowledge bases : a context-based approach (Best Paper Award). Proceedings of the 7th SDS. https://doi.org/10.1109/SDS49233.2020.00013
• Nadig, S., Braschler, M., & Stockinger, K. (2020, May). Database search vs. information retrieval : a novel method for studying natural language querying of semi-structured data. Proceedings of the 12th LREC. https://doi.org/10.21256/zhaw-20042
• Brunner, U., & Stockinger, K. (2020). Entity matching with transformer architectures - a step forward in data integration [Conference paper]. Proceedings of EDBT 2020, 463–473. https://doi.org/10.5441/002/edbt.2020.58
• Mendes de Farias, T., Stockinger, K., & Dessimoz, C. (2019). VoIDext : vocabulary and patterns for enhancing interoperable datasets with virtual links [Conference paper]. OTM 2019 Conference Proceedings, 607–625. https://doi.org/10.1007/978-3-030-33246-4_38
• Brunner, U., & Stockinger, K. (2019). Entity matching on unstructured data : an active learning approach [Conference paper]. Proceedings of the 6th SDS, 97–102. https://doi.org/10.1109/SDS.2019.00006
• Stockinger, K., Heitz, J., Bundi, N. A., & Breymann, W. (2018). Large-scale data-driven financial risk modeling using big data technology [Conference paper]. 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), 206–207. https://doi.org/10.1109/BDCAT.2018.00033
• Sima, A.-C., Stockinger, K., Affolter, K., Braschler, M., Monte, P., & Kaiser, L. (2018). A hybrid approach for alarm verification using stream processing, machine learning and text analytics. Proceedings of the 21st International Conference on Extending Database Technology. https://doi.org/10.21256/zhaw-3487
• Graf, H. D., Koc, Y., Panighetti, S., Togni, M., von Grünigen, D., Weilenmann, M., Xhoxhaj, E., Zürrer, D., Benites de Azevedo e Souza, F., Deriu, J. M., Neureiter, N., von Däniken, P., Cieliebak, M., Eich, W., Neuhaus, S., & Stockinger, K. (2017). Four different ways to build a chatbot about movies. SwissText 2017: 2nd Swiss Text Analytics Conference, Winterthur, 9. Juni 2017.
• 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

• Saxer, J., Aigner, I. M., Linzmeier, L., Weiler, A., & Stockinger, K. (2025, December 19). Query carefully : detecting the unanswerables in text-to-SQL tasks. International Joint Workshop of Artificial Intelligence for Healthcare (HC@AIxIA) and HYbrid Models for Coupling Deductive and Inductive ReAsoning (HYDRA), Bologna, Italy, 25-26 October 2025. https://doi.org/10.48550/arXiv.2512.21345
• Chen, Y., Vergara, A. F., Hamilton, A., & Stockinger, K. (2024). Digital public infrastructure for environmental sustainability. United Nations Environment Programme. https://doi.org/10.21256/zhaw-30874
• Stampfli, J., & 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, 10. https://doi.org/10.21256/zhaw-3785
• Stockinger, K., van Lingen, F., & Valente, M. (2015). Big data analytics in a connected world. Business Intelligence Journal, 20(2). https://tdwi.org/research/2015/06/business-intelligence-journal-vol-20-no-2.aspx
• 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
• Imhof, M., Looser, J., Musy, T., & Stockinger, K. (2014). Evaluation of query processing with impala for mixed workloads. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-3786
• Stockinger, K. (2013). Data Scientists : die neuen Helden des 21. Jahrhunderts? Tages-Anzeiger, 29.

Mündliche Konferenzbeiträge und Abstracts

• Fankhauser, T., Soler, M., Füchslin, R. M., & Stockinger, K. (2021). Implementing database and machine learning algorithms on publicly available quantum computers. Talk at Zurich University, Zurich, Switzerland, 4 May 2021.
• Weber, R., Mehmet, Y., Füchslin, R. M., & Stockinger, K. (2020, September 9). Quantum databases and quantum machine learning : how far can we go on a publicly available quantum computer? Datalab Seminar ZHAW, Winterthur, 9. September 2020. https://www.zhaw.ch/de/forschung/departementsuebergreifende-kooperationen/datalab/datalab-seminar/
• Breymann, W., & Stockinger, K. (2014). Data-driven financial system modeling (DatFisMo). 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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