Wolfgang Trutschnig's homepage

Prof. Dr. Wolfgang Trutschnig curriculum vitae (status 2023-03)

  Professor for Stochastics & Director IDA Lab Salzburg
  Department for Artificial Intelligence & Human Interfaces (AIHI)
  University of Salzburg
  Hellbrunner Strasse 34
  A-5020 Salzburg
  Austria
  Tel: +43 662 8044 5326
  Email: wolfgang@trutschnig.net
  Profiles: Google scholar Scopus Author ID: 25652347200 ORCID: 0000-0002-7131-1944 zbMATH entry

  Co-Managing Director correlate analytics gmbh

Job opening 2023-03: Postdoc Position Dependence Modeling or Nonparametric Statistics @University of Salzburg (basic and applied research)

Calculating and estimating the extent of multivariate (directed) dependence - new article:

[47] F. Griessenberger, R.R. Junker, W. Trutschnig: On a multivariate copula-based dependence measure and its estimation, Electronic Journal of Statistics 16, 2206-2251 (2022), doi:10.1214/22-EJS2005 preprint (pdf)

Why the so-called simplifying assumption is not as flexible as frequently praised - new article:

[43] T. Mroz, S. Fuchs, W. Trutschnig: How simplifying and flexible is the simplifying assumption in pair-copula constructions - analytic answers in dimension three and a glimpse beyond, Electronic Journal of Statistics 15, 1951-1992 (2021), doi:10.1214/21-EJS1832 preprint (pdf)

Oberseminar Analysis, Mathematische Physik & Dynamische Systeme
June 29, 2021, via zoom (slides)

10th International Workshop on Simulation and Statistics
September 02-06, 2019, Salzburg, Austria (slides)

Symposium: Ecology - Geomorphology - Statistics
March 28-29, 2018, Salzburg, Austria (slides for general audience)

11th International Conference of the ERCIM WG on Computational and Methodological Statistics (CMStatistics 2018)
December 14-16, 2018, Pisa, Italy

International Statistics Festival Ulm
September 17-18, 2018, Ulm, Germany (slides)

Research Interests

Probability Theory & Mathematical Statistics, Fractals, Analysis

Copulas and Dependence Models
Multivariate and Nonparametric Statistics
Dynamical Systems (in discrete time) and their interplay with number theory
Fractals, Singular Functions and Iterated Function Systems
Markov Operators and Kernels
Lineability, spaceability, algebrability, latticeability

Applied Statistics & Data Science

Probabilistic fundamentals of Machine Learning
Forecasting and Regression Techniques
Feature Selection
Dependence Modeling of processes
Automatic Reporting with R and Miktex (knitR, Sweave)
Interactive Dashboards with shiny

pic as pdf

   pic as png
   pic as pdf (joint work with Manuela Schreyer)

   slides Fraktale und Zufall
   (Tag der Mathematik, PH-Salzburg)