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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2020.
Wiesbaden:
Springer.
ISBN 978-3-658-28414-5.
Available from: https://doi.org/10.1007/978-3-658-28415-2
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Klinkert, Andreas; Fusek, Peter; Riesen, Bruno; Berner, Roman,
2019.
Automated airport staff scheduling at Swissport International Ltd..
IFORS News.
13(4), pp. 2-4.
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2019.
Big Data und Machine Learning in Industrie 4.0 : Perspektiven für Service-Modelle.
KunststoffXtra.
9(12), pp. 19-21.
Available from: https://issuu.com/sigwerbgmbh/docs/web_kx_12-2019/1?ff&showOtherPublicationsAsSuggestions=true&backgroundColorFullscreen=%23e8edf0
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Wüst, Raimond Matthias; Bütikofer, Stephan; Köchli, Joël; Ess, Severin,
2019.
In:
Proceedings of the 2nd International Railway Symposium Aachen 2019.
IRSA 2019 : 2nd International Railway Symposium, Aachen, Germany, 26-28 November 2019.
Aachen:
RWTH Aachen University.
pp. 226-244.
Available from: https://doi.org/10.18154/RWTH-2019-11969
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Wildi, Marc; Bundi, Nils Andri; et al.,
2019.
Bitcoin and market-(in)efficiency : a systematic time series approach.
Digital Finance.
1(1), pp. 47-65.
Available from: https://doi.org/10.1007/s42521-019-00004-z