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Students

Current PhD students:

  1. Yan Pan: High-dimensional BSS using robust second order statistics. (University of Jyväskylä, jointly with Sara Taskinen)
  2.  Mika Sipilä: Nonlinear blind source separation for spatial and spatio-temporal data. (University of Jyväskylä, jointly with Sara Taskinen)

Previous PhD students:

  1.  Jari Miettinen (2014): On statistical properties of blind source separation methods based on joint diagonalization. (University of Jyväskylä, jointly with Sara Taskinen)
  2. Aurore Archimbaud (2018): Statistical methods for outlier detection for high-dimensional data. (Toulouse School of Economics, jointly with Anne Ruiz-Gazen)
  3. Markus Matilainen (2018): On independent component analysis and supervised dimension reduction for multivariate time series. (University of Turku, jointly with Hannu Oja)
  4. Joni Virta (2018): Independent component analysis for non-standard data structures. (University of Turku, jointly with Hannu Oja and Bing Li)
  5. Christoph Mühlmann (2021): Advances in blind source separation for spatial data. (TU Wien)
  6. Una Radojicic (2021): Non-Gaussian feature extraction for complex data. (TU Wien)

Currently supervised Master theses:

  1. Katja Palomäki: Robust linear mixed models for the analyis of myopia data. (University of Jyväskylä, jointly with S. Taskinen).
  2. Juuso Koskinen: Statistical analysis of MEG data to reveal psychological features. (University of Jyväskylä, jointly with S. Taskinen and T. Parviainen).
  3. Antero Heikkilä: Linear mixed models in high dimensions. (University of Jyväskylä, jointly with S. Taskinen and I. Stranden).

Previously supervised Master’s theses:

  1. Merri Markkanen (2023): Comparison of three different ordinal logistic regression methods for predicting person’s self-assessed health status with functional and haemodynamic covariates. (University of Jyväskylä, jointly with S. Taskinen).
  2. Christoph Kösner (2023): On estimating the signal dimension in tensorial PCA. (TU Wien, jointly with J. Virta)
  3. Mika Sipilä (2022): Newton update based independent vector analysis with various source density models. (University of Jyväskylä, jointly with S. Taskinen).
  4. Luzia Jorda (2021): Spatial blind source separation for soil moisture data. (TU Wien, jointly with C. Mühlmann)
  5. Martin Hofbauer (2020): Log-concave density estimation and its application in a use case in the semiconductor industry. (TU Wien)
  6. Gregor Fischer (2020): Blind source separation for compositional time series. (TU Wien, jointly with P. Filzmoser)
  7. Lea Flumian (2019): On stationary subspace analysis. (TU Wien)
  8. Juho Pelto (2016): Estimation of signal space in principal components analysis. (University of Turku, jointly with H. Oja)
  9. Matti Rytkönen (2014): On multivariate regression using spatial signs and ranks. (University of Tampere)
  10. Simo Korpela (2013): Comparing the performance of multivariate location tests for Lp-norm distributed data. (University of Tampere)
  11. Päivi Julin (2013): Palvelu- ja toimitilarakentamisen ennakoivat indikaattorit ja ennustamisen mallintaminen. (University of Tampere, jointly with A. Luoma)
  12. Eero Liski  (2009): On sliced inverse regression. (University of Tampere, jointly with H. Oja)

Currently supervised Bachelor theses:

  1. –

Previously supervised Bachelor’s theses:

  1. Katharina Riemer (2023): Computing M-estimators of location and shape in R when data is missing. (TU Wien, jointly with U. Radojicic)
  2. Krisztian Harmat (2021): On blind source separation methods and deep learning for multivariate time series prediction. (TU Wien, jointly with C. Mühlmann)
  3. Thomas Janka (2021): A Shiny app for exploratory projection pursuit. (TU Wien)
  4. Markus Mayrhofer (2020): Statistische Analyse von Wechselrichter-Thermotests (TU Wien)
  5. Max Griesmayer (2019): Automatic outlier detection for haemodynamic tilt-table data. (TU Wien)
  6. Christoph Kösner (2019): Computational aspects of kernel ICA. (TU Wien)
  7. Matthäus Kerres (2018): “Prophet” – time series predition using generalized additive models. (TU Wien)