Tatjana Pavlenko
Professor at Statistiska institutionen
- E-mail:
- tatjana.pavlenko@statistik.uu.se
- Visiting address:
- Ekonomikum (plan 3)
Kyrkogårdsgatan 10 - Postal address:
- Box 513
751 20 UPPSALA
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Short presentation
About me
Professor in Statistics. My research interests lie in the area of statistical inference and applied probability 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.
You can find most of my publications on the Google scholar.
Keywords
- high-dimensional statistical inference
- large-scale biomedical data
- multivariate data more broadly
- sparse signal detection
- statistical machine learning
Research
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.
Uppsala University provides me with an opportunity to continue to work with my ideas in the frame of AI4Research, an exiting project which focuses on strengthening, renewing and developing research in Artificial Intelligence and machine learning.
For a more thorough description of my research or questions feel free to send me an email.
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.
Publications
Recent publications
- A Behrens-Fisher problem for general factor models in high dimensions (2023)
- Sequential sampling of junction trees for decomposable graphs (2022)
- Adaptive threshold-based classification of sparse high-dimensional data (2022)
All publications
Articles
