University of Glasgow and CLDS
my_name 'dot' my_surname 'at' glasgow 'dot' ac 'dot' uk
Welcome to my website!
I am a research associate in Statistics at the School of Mathematics and Statistics at the University of Glasgow, working with Dirk Husmeier on computer-intensive statistical inference in soft-tissue mechanical models based on partial differential equations. My current post is within the EPSRC funded Closed-Loop Data Science centre, where I work on incorporating feedback effects into healthcare prognostication systems. My previous position at Glasgow was within SofTMech, an EPSRC centre for Multiscale Soft Tissue Mechanics with applications to heart and cancer.
I did my PhD at the Department of Econometrics and OR, Vrije Universiteit Amsterdam, and the Tinbergen Institute. I was working Siem Jan Koopman and Lennart Hoogerheide 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.
My main research interests:
- Bayesian methods
- Monte Carlo methods (MCMC, SMC, ABC)
- Time series analysis (especially state space models)
- Data analysis (including rare event estimation)
- Machine learning (e.g. Bayesian optimisation)
Besides, I am interested in code optimization, deep learning and adaptive importance sampling.
|Oct 9, 2021||On the 7th of November 2021 I will talk about our paper Neural Network-Based Left Ventricle Geometry Prediction from CMR Images with Application in Biomechanics at the ML in PL 2021 Conference.|
|Aug 3, 2021||Our paper Neural Network-Based Left Ventricle Geometry Prediction from CMR Images with Application in Biomechanics has been accepted in Artificial Intelligence in Medicine. The journal version is available here.|
|Mar 23, 2021||Our paper Inference in cardiovascular modelling subject to medical interventions has been accepted in the proceedings of ICSTA’21. This is a part of my current research agenda within the Closed-Loop Data Science centre.|
|Jan 27, 2021||I will present our paper on GP-enhanced ABC for chemotaxis at the mini-symposium Stochastic models in biology informed by data during the British Applied Mathematics Colloquium (BAMC).|
|Nov 8, 2020||Our paper on GP-enhanced ABC for chemotaxis has just been accepted in Journal of Computational Physics! The accepted version is here.|
|Sep 17, 2020||Our paper on Bayesian optimisation for cardiac mechanics models has just been submitted, the final version is here.|
|Mar 12, 2020||Due to the Covid-19 outbreak both the ABC in Grenoble Workshop and the British Applied Mathematics Colloquium will unfortunately not take place as planned, with the former having been cancelled and the latter postponed until 2021. Please all take care!|
|Feb 28, 2020||I will give a talk on Gaussian Process Enhanced Semi-Automatic ABC: Parameter Inference in a Stochastic Differential Equation System for Chemotaxis at the ABC in Grenoble Workshop, 19-20 March 2020.|
|Feb 17, 2020||I will give a talk on Gaussian Process Enhanced Semi-Automatic ABC: Parameter Inference in a Stochastic Differential Equation System for Chemotaxis at the British Applied Mathematics Colloquium, at the Symposium on “Stochastic models in biology informed by data”.|
|Feb 13, 2020||The final version of our Partially Censored Posterior for Robust and Efficient Risk Evaluation is available online on Journal of Econometrics website.|