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ZK35 - High-dimensional statistical learning: New methods to advance economic and sustainability policy

The figure illustrates results from an urban mobility application, where we investigated the predicted probability that a mobile phone network can withstand critical events at three different times of the day at different locations in Milan, Italy. The grid corresponds to the center of Milan.

The figure illustrates results from an urban mobility application, where we investigated the predicted probability that a mobile phone network can withstand critical events at three different times of the day at different locations in Milan, Italy. The grid corresponds to the center of Milan.

In this project, we aim to investigate how the largely separate research streams of Bayesian econometrics, statistical model checking, and machine learning can be combined and integrated to create innovative and powerful tools for the analysis of big data in economics and other social sciences. The computational statistics group at TU Wien is focusing on dimensionality reduction methods in high dimensions and on the collaboration with the cyber-physical systems group at TU, which aims to build an interdisciplinary framework for explainable algorithms.

 

Coordinator: Gregor Kastner, University of Klagenfurt

Partners: Alpen-Adria University Klagenfurt, Paris Lodron University Salzburg, TU Wien, WIFO (Austrian Institute of Economic Research)

TU Wien team: Roman Parzer, Laura Vana-Gür

 

Program / Call: FWF Zukunkftskolleg Grant

Funding: FWF Austrian Science Fund

Start: 01 August 2019, duration: 4 years

Project web page:https://zk35.org/, opens an external URL in a new window