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New R packages

during the spring we published several new R packages on CRAN. KernelICA gives an implementation of kernel ICA, and ssaBSS has several methods for (non-)stationary source separation. shapeNA allows for robust shape estimation when there are missing values in the data.

Meanwhile I am involved in 22 packages on CRAN. Below a figure about their monthly download stats as provided from the package dlstats.


Since beginning of the month I am tenured!

At that occassion I would also like to point out that many new papers are out. See my publication list for details. To give an impression of my research I also added below a word cloud of the abstracts of my published articles.

Invited speaker at ICORS 2019

I’m heading to ICORS as an invited speaker and will give a talk about robust approaches for blind source separation. This will be the first time I get to visit Ecuador!

400 000€ Grant!

Great news! The Austrian Science Fund (FWF) will fund my
project “Blind Source Separation in Time and Space” with almost 400000 Euros.

Several new papers out

It has been a while since my last post – several papers have appeared since then, like

  1. “Supervised Dimension Reduction for Multivariate Time Series” appeared in  Econometrics and Statistics.
  2. “Independent Component Analysis for Tensor-valued Data” appeared in the Journal in the Multivariate Analysis. For a short time it can be downloaded at here.
  3. I presented our paper “Blind Source Separation For Nonstationary Tensor-Valued Time Series” at the IEEE International Workshop on Machine Learning for Signal Processing. It should appear soon IEEE Xplore.

More details are also on my list of publications.

End of the year rush

The end of 2016 was busy but productive. Two more papers were uploaded to Arxiv:

  1.  “Projection Pursuit for non-Gaussian Independent Components
  2. Multivariate Outlier Detection with ICS

and two R packages were uploaded to CRAN:

  1. ICSOutlier which implements the methods from paper 2 above.
  2. ICtest which provides the methods from paper 1 above and from our recently submitted paper “Asymptotic and bootstrap tests for subspace dimension“.

Feedback and comments are welcome!