I am a PhD candidate and junior researcher in Bayesian computation under the supervision of Prof. Krzysztof Latuszynski and Prof. Gareth Roberts. My goal is to contribute to the development of computational methodology that enables exact Bayesian inference for complex inference problems in the sciences and AI. I also bring an applied perspective to my work due to my background in Economics and my industry experience as a Data Scientist. That perspective informs my interest in translating methodological research into plug-and-play tools that are accessible to other researchers.
Areas of research
- Monte Carlo sampling methods for intractable models, e.g. diffusion processes.
- Scalable Bayesian computation.
O. Papaspiliopoulos, T. Stumpf-Fetizon and G. Zanella, Scalable computation for Bayesian hierarchical models. Pre-print: arXiv:2103.10875
- J. Garcia Montalvo, O. Papaspiliopoulos and T. Stumpf-Fetizon, Bayesian forecasting of electoral outcomes with new parties’ competition, European Journal of Political Economy, 2019. doi: 10.1016/j.ejpoleco.2019.01.006
- Infemus: An Infinite Dimensional Method for Marginal Likelihood Estimation, SIAM Uncertainty Quantification, 2022-04-13.
- Exact Bayesian Inference for Markov Switching Diffusions, Current Developments in MCMC Methods, 2021-12-09.
- Exact Bayesian Inference for Markov Switching Diffusions, Warwick Statistics Algorithms Seminar, 2021-06-11.
Statistical Methods for Election Forecasting, Barcelona GSE Data Science Seminar, 2020-01-31.
- ST111 Probability A / ST112 Probability B (T2 2021-22)
- ST219 Mathematical Statistics B (T2 2019-20)
- ST333 Applied Stochastic Processes (T1 2019-20)
ST404 Applied Statistical Modelling (T2 2018-19)
17DS04 Bayesian Machine Learning in Social Sciences (Barcelona GSE, T3 2016-17)
14D001 Statistical Modelling and Inference (Barcelona GSE, T2 2016-17)
- PhD course representative (2019-2020)
- Young researcher's meeting co-organizer (2020-2021)