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
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Team IDP
Publikationen
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Fačevicová, Kamila; Hron, Karel; Todorov, Valentin; Templ, Matthias,
2016.
Compositional Tables Analysis in Coordinates.
Scandinavian Journal of Statistics.
pp. 962-977.
Available from: https://doi.org/10.1111/sjos.12223
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Stockinger, Kurt; Stadelmann, Thilo; Ruckstuhl, Andreas,
2016.
In:
Fasel, Daniel; Andreas, Meier, eds.,
Big Data.
Wiesbaden:
Springer.
pp. 59-81.
Edition HMD.
Available from: https://doi.org/10.1007/978-3-658-11589-0_4
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Templ, Matthias,
2016.
In:
eRum 2016, European R Users Meeting, Poznan, Poland, 12-14 October 2016.
Available from: http://erum.ue.poznan.pl/
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Heitz, Christoph; Goren, Lilach; Sigrist, Jörg,
2016.
In:
Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015).
WCEAM 2015, Tampere, Finland, 28-30 September 2015.
Cham:
Springer.
pp. 259-268.
Lecture Notes in Mechanical Engineering.
Available from: https://doi.org/10.1007/978-3-319-27064-7_25
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Hu, Yang; Palmé, Thomas; Fink, Olga,
2016.
Deep health indicator extraction : a method based on auto-encoders and extreme learning machines[paper].
In:
PHM 2016 : Proceedings of the Annual Conference of the Prognostics and Health Management Society 2016.
PHM 2016, Denver, USA , 3-6 October 2016.
PMH Society.
pp. 446-452.
Available from: https://doi.org/10.21256/zhaw-2756