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
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
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Team IDP
Publikationen
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Chen, Ying; Giudici, Paolo; Hadji Misheva, Branka; Trimborn, Simon,
2020.
Lead behaviour in Bitcoin markets.
Risks.
8(1), pp. 4.
Available from: https://doi.org/10.3390/risks8010004
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Bödi, Linda Helen; Grabner, Helmut,
2020.
Learning to ignore : fair and task independent representations.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-21602
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Grize, Yves-Laurent; Fischer, Wolfram; Lützelschwab, Christian,
2020.
Machine learning applications in nonlife insurance.
Applied Stochastic Models in Business and Industry.
36(4), pp. 523-537.
Available from: https://doi.org/10.1002/asmb.2543
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Grize, Yves-Laurent; Fischer, Wolfram; Lützelschwab, Christian,
2020.
Machine learning applications in non‐life insurance : discussion rejoinder.
Applied Stochastic Models in Business and Industry.
36(4), pp. 545-547.
Available from: https://doi.org/10.1002/asmb.2564
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Templ, Matthias,
2020.
Modeling and prediction of the impact factor of journals using open-access databases.
Austrian Journal of Statistics.
49(5), pp. 35-58.
Available from: https://doi.org/10.17713/ajs.v49i5.1186