a 'dot' borowska2 'at' vu 'dot' nl

Welcome to my website! I am an adjunct faculty at the School of Business and Economics, Vrije Universiteit Amsterdam. My research interests lie in the area of Bayesian and computational statistics and econometrics. My work has focused on developing efficient sampling-based algorithms for prediction and inference in different contexts, such as time series forecasting, risk evaluation, state space models, and high-dimensional problems with intractable likelihood.

Previously I was a research associate in Statistics at the School of Mathematics and Statistics, University of Glasgow, working with Dirk Husmeier on computer-intensive statistical methods for soft-tissue mechanical models based on partial differential equations. I worked within two EPSRC funded projects, the Closed-Loop Data Science (CLDS) centre and SofTMech. My CLDS work concentrated on incorporating feedback effects into healthcare prognostication systems. In SofTMech, my research was mostly related to inference and uncertainty quantification in biomechanical models.

I did my PhD at the Department of Econometrics and OR (now the School of Business and Economics), Vrije Universiteit Amsterdam, and the Tinbergen Institute, under the supervision of Siem Jan Koopman and Lennart Hoogerheide. I worked on developing methods for accurate and efficient Bayesian inference for time series analysis, with a particular focus on applications in Econometrics and Finance. In Spring 2017 I visited Ruth King at the School of Mathematics, the University of Edinburgh, to work on efficient algorithms for Bayesian inference in state space models.

I hold a Bachelor’s degree in Mathematics from the Faculty of Mathematics, Informatics, and Mechanics, the University of Warsaw, and a Master’s degree in Economics from the Warsaw School of Economics.

In 2021 and 2023 I was on maternity leave.