Institute of Data Analysis and Process Design (IDP)
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 Envrionmental Analytics
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 IDP
Publikationen
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2023.
Deep learning for predictive maintenance : scalable implementation in operational setups.
In:
10th IEEE Swiss Conference on Data Science (SDS), Zurich, Switzerland, 22-23 June 2023.
Available from: https://sds2023.ch/deep-learning-for-predictive-maintenance
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Baumann, Joachim; Loi, Michele,
2023.
Fairness and risk : an ethical argument for a group fairness definition insurers can use.
Philosophy & Technology.
36(45).
Available from: https://doi.org/10.1007/s13347-023-00624-9
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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