Tatjana Pavlenko

Tatjana Pavlenko's research interests lie in the area of applied probability and statistical inference in high and infinite dimensions, computational statistics - especially Bayesian graph structure learning, high-dimensional problems in statistical machine learning, detection and identification of sparse signals, with applications to large-scale biomedical data.

Google scholar link

I am working with methodology and theory in the field of mathematical statistics, with the main focus on the development of inferential and algorithmic procedures for complex, high-dimensional data.

Lately I have been increasingly interested in the theoretical foundation of the modeling and and analysis of high-dimensional data with sparsity patterns, and the interplay with ideas from the theory of weighted quantile and empirical processes. The aim of the research is, amongst other things, to develop the theory, tools and algorithms of sparse representations, and to provide optimally adaptive, data-driven procedures in various areas of statistical learning.

Current interests include: computational statistics and algorithmic inference for sparse data, statistical learning theory, large-scale statistical inference.

My PhD students:

Albin Toft, Division of Mathematical Statistics, KTH Royal Institute of Technology. Tentative
thesis title: High-Dimensional Causal Inference in Media. Planned dissertation date: 2025.

Felix Leopoldo Rios, 2012-2017. Bayesian inference in probabilistic graphical models. Currently working at the Department of Mathematics and Informatics, University of Basel (Switzerland).

Annika Tillander, 2009-2013. Classification models for high-dimensional data with sparsity patterns. Currently working as a senior lecturer at the Department of Computer and Information Science, Division of Statistics and Machine Learning, Linköping University.