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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2019.
Daten in der Instandhaltung : Potenziale und Stolpersteine.
fmpro service.
2019(1), pp. 44-47.
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Meierhofer, Jürg; Lév, Jana,
2019.
Daten-basierter Service-Nutzen entlang der Customer Journey.
ServiceToday.
33(2), pp. 86-87.
Available from: https://doi.org/10.21256/zhaw-3243
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Inan, Berkay; Cernak, Milos; Grabner, Helmut; Tukuljac, Helena Peic; Pena, Rodrigo C. G.; Ricaud, Benjamin,
2019.
Evaluating audiovisual source separation in the context of video conferencing[paper].
In:
Proceedings Interspeech 2019.
Interspeech 2019, Graz, Austria, 15-19 September 2019.
International Speech Communication Association (ISCA).
pp. 4579-4583.
Available from: https://doi.org/10.21437/Interspeech.2019-2671
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Templ, Matthias; Gussenbauer, J.; Filzmoser, P.,
2019.
Evaluation of robust outlier detection methods for zero-inflated complex data.
Journal of Applied Statistics.
47(7), pp. 1144-1167.
Available from: https://doi.org/10.1080/02664763.2019.1671961
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Ahelegbey, Daniel Felix; Giudici, Paolo; Hadji Misheva, Branka,
2019.
Factorial network models to improve P2P credit risk management.
Frontiers in Artificial Intelligence.
2, pp. 8.
Available from: https://doi.org/10.3389/frai.2019.00008