Institute for Data Science (IDS)
We create value from data
We use advanced data-based methods to create innovative solutions for business and industry. We address real-life challenges with scientific methods and a strong commitment to practicability. We are the leading educator and partner of choice for applied data science and business engineering in Switzerland.
Research Groups
Advanced scientific tools for solutions in the financial industry
Health and Environmental Analytics
Analyzing data to derive interpretable results using statistical and machine learning techniques
Maintenance, Mobility, AI & Society
Leverage AI and advanced modeling for innovations in predictive maintenance, mobility solutions, and socially aligned systems
Generating insights, creating value and fostering innovation in business processes and services
Visual Intelligence and Applications
As visual data becomes one of the most abundant and complex sources of information, Visual Intelligence is a key pillar of modern data science — enabling new ways to analyze, model, and communicate through images, video, and immersive environments
For Students
Consulting Services
News
Team IDS
Publikationen
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Baumann, Joachim; Castelnovo, Alessandro; Crupi, Riccardo; Inverardi, Nicole; Regoli, Daniele,
2023.
Bias on demand : a modelling framework that generates synthetic data with bias[paper].
In:
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency.
6th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Chicago, USA, 12-15 June 2023.
Association for Computing Machinery.
pp. 1002-1013.
Available from: https://doi.org/10.1145/3593013.3594058
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Arpogaus, Marcel; Voss, Marcus; Sick, Beate; Nigge-Uricher, Mark; Dürr, Oliver,
2023.
Short-term density forecasting of low-voltage load using bernstein-polynomial normalizing flows.
IEEE Transactions on Smart Grid.
14(6), pp. 4902-4911.
Available from: https://doi.org/10.1109/TSG.2023.3254890
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Schweiger, Lukas; Barth, Linard,
2023.
Properties and characteristics of digital twins : review of industrial definitions.
SN Computer Science.
4(5), pp. 436.
Available from: https://doi.org/10.1007/s42979-023-01937-4
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Sedding, Helmut; Seidel, Maxim,
2023.
Lot sizing and scheduling of injection molding machines with setup resources and demand uncertainty[paper].
In:
IWDSP 2023 : The Fourth International Workshop on Dynamic Scheduling Problems, Winterthur, Switzerland, 5-6 June 2023.
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Gawiejnowicz, Stanisław; ZHAW Zurich University of Applied Sciences; Adam Mickiewicz University Poznan, eds.,
2023.
The 4th International Workshop on Dynamic Scheduling Problems : Extended Abstracts.
The 4th International Workshop on Dynamic Scheduling Problems (IWDSP), Winterthur, Switzerland, 5-6 June 2023.
Warsaw:
Polish Mathematical Society.
.
ISBN 978-83-962157-1-0.
Available from: https://doi.org/10.14708/isbn.978-83-962157-1-0