Curriculum vitae

Education

2011 Bachelor in Economics, University of Luxembourg, very good.
2013 Master in Econometrics, University of Louvain, magna cum laude.
2015 Master in Statistics, University of Louvain, magna cum laude.
2019 Master in Biostatistics, University of Louvain, magna cum laude.
2020 PhD in Statistics, “Laplace Approximations and Bayesian P-splines for Statistical Inference”, under the supervision of Prof. Philippe Lambert, University of Louvain PhD-thesis.
2020- Postdoctoral researcher, Data Science Institute, Hasselt University.

Research

1. Gressani, O. and Lambert, P. (2018). Fast Bayesian inference using Laplace approximations in a flexible promotion time cure model based on P-splines. Computational Statistics and Data Analysis, 124, 151-167. doi.org/10.1016/j.csda.2018.02.007

2. Gressani, O. and Lambert, P. (2021). Laplace approximations for fast Bayesian inference in generalized additive models based on P-splines. Computational Statistics and Data Analysis, 154, 107088. doi.org/10.1016/j.csda.2020.107088

3. Gressani, O., Faes, C. and Hens, N. (2022). Laplacian-P-splines for Bayesian inference in the mixture cure model. Statistics in Medicine, 41(14), 2602-2626. doi.org/10.1002/sim.9373

4. Gressani, O., Wallinga, J., Althaus, C., Hens, N. and Faes, C. (2022). EpiLPS: A fast and flexible Bayesian tool for estimation of the time-varying reproduction number. PLoS Computational Biology, 18(10): e1010618.
doi.org/10.1371/journal.pcbi.1010618

5. Gressani, O., Faes, C. and Hens, N. (2023). An approximate Bayesian approach for estimation of the instantaneous reproduction number under misreported epidemic data. Biometrical Journal, 65(6): 2200024.
doi.org/10.1002/bimj.202200024

6. Vandendijck, Y., Gressani, O., Faes, C., Camarda, C.G. and Hens, N. (2023). Cohort-based smoothing methods for age-specific contact rates. (2023) Biostatistics, 25(2):521-540. doi.org/10.1093/biostatistics/kxad005

7. Lambert, P., Gressani, O. (2023). Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models. Statistical Modelling, 23(5-6):409-423. doi.org/10.1177/1471082X231181173

8. Moreels, N., Boven, A., Gressani, O., Andersson, F.L., Vlieghe, E., Callens, S., Engstrand, L., Simin, J., Brusselaers, N. (2024). The combined effect of systemic antibiotics and proton pump inhibitors on Clostridioides difficile infection and recurrence. Journal of Antimicrobial Chemotherapy, 79(3):608-616. doi.org/10.1093/jac/dkae012

9. Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2023). Bayesian nowcasting with Laplacian-P-splines. MedRxiv preprint. 10.1101/2022.08.26.22279249v2

10. Gressani, O., Torneri, A., Hens, N. and Faes, C. (2023). Flexible Bayesian estimation of incubation times. MedRxiv preprint. 10.1101/2023.08.07.23293752

11. Sumalinab, B., Gressani, O., Hens, N. and Faes, C. (2023). An efficient approach to nowcasting the time-varying reproduction number. MedRxiv preprint. 10.1101/2023.10.30.23297251

Software

1. Gressani, O. and Lambert, P. (2020). The blapsr package for fast inference in latent Gaussian models by combining Laplace approximations and P-splines. CRAN. https://cran.r-project.org/package=blapsr

2. Gressani, O. (2021). A package for approximate Bayesian inference in mixture cure models with Laplacian-P-splines. https://github.com/oswaldogressani/mixcurelps

3. Gressani, O. (2021). EpiLPS: A fast and flexible Bayesian tool for estimating epidemiological parameters. CRAN. https://cran.r-project.org/package=EpiLPS

Conferences and Visits

1. 16th Annual Conference of Public Economic Theory, Luxembourg, 02-04 July 2015: 'Endogeneous Quantal Response Equilibrium in Normal Form Games' (Contributed talk).

2. 37th Annual Conference of the International Society for Clinical Biostatistics; Birmingham (UK), 21-25 August 2016: 'Approximate Bayesian methods in cure survival models: Coupling P-splines with Laplace approximations for fast inference' (Contributed talk).

3. Visiting researcher at Basque Center for Applied Mathematics (BCAM); Bilbao (Spain), 10-13 December 2017.

4. Survival Analysis for Junior Researchers (SAfJR) conference, Leiden (The Netherlands), 24-26 April 2018: 'P-splines and Laplace approximations for fast Bayesian inference in a flexible promotion time cure model' (Poster presentation).

5. International Society for Bayesian Analysis (ISBA) World Meeting, Edinburgh (UK), 24-29 June 2018: 'Merging Markov chain Monte Carlo with Laplace approximations for fast inference in Generalized additive models' (Poster presentation).

6. 26th Annual Meeting of the Royal Statistical Society of Belgium (RSSB), Ovifat (Belgium), 17-19 October 2018: 'Bridging the gap between Bayesian P-splines and Laplace’s method for inference in Generalized additive models' (Contributed talk).

7. 40th Annual Conference of the International Society for Clinical Biostatistics (ISCB), Leuven (Belgium), 14-18 July 2019: 'Unifying Laplace’s method and Bayesian penalized regression splines for estimation in generalized additive models' (Contributed talk).

8. Invited talk at the Statistics Seminar of the Institut de Mathématiques de Marseille I2M (France), 7 December 2020: 'Laplace-P-splines for approximate Bayesian inference'.

9. 42nd Annual Conference of the International Society for Clinical Biostatistics (ISCB), Lyon (France), 18-22 July 2021: 'Laplace approximations for fast Bayesian inference of the time-varying reproduction number under misreported epidemic data' (Contributed talk).

10. 28th Annual Meeting of the Royal Statistical Society of Belgium (RSSB), Liège (Belgium), 21-22 October 2021: 'The EpiLPS project: a new Bayesian tool for estimating the time-varying reproduction number' (Contributed talk).

11. Data Science Institute (DSI) Seminar, Hasselt University, (Belgium), 22 June 2022: 'The power of Laplacian-P-splines'.

12. 36th International Workshop on Statistical Modelling (IWSM), Trieste (Italy), 18-22 July 2022: 'The power of Laplacian-P-splines for inference in epidemiological and survival models' (Contributed talk).

13. Invited talk at the seminar organized by the Center for Computational and Stochastic mathematics (CEMAT) and the Center for Statistics (CEAUL), University of Lisbon, 12 October 2022: 'Approximate inference with Bayesian P-splines in epidemic models'.

14. Visiting researcher at Basque Center for Applied Mathematics (BCAM); Bilbao (Spain), 27-30 November 2022.

15. International Conference on Computational and Financial Econometrics (CFE) & Computational and Methodological Statistics (CMStatistics), King's College London (UK), 17-19 December 2022: 'Approximate Bayesian inference in epidemic models: A focus on nowcasting and the time-varying reproduction number' (Contributed talk).

16. 30th Annual Meeting of the Royal Statistical Society of Belgium (RSSB), Louvain-la-Neuve (Belgium), 19-20 October 2023: 'Flexible Bayesian estimation of incubation times' (Contributed talk).

17. Invited talk at the Computational Bayesian Statistics Seminar, Flatiron Institute, New York (USA), 27 October 2023: 'Bayesian inference with Laplacian-P-splines: A methodology for fast and flexible estimation of key epidemiologic parameters' [Slides].

Awards and Honors

2014-2015 AFR grant from the National Research Fund Luxembourg.
2014-2015 Top of the class in the research Master in Statistics (University of Louvain).

Activities

Reviewed for the following journals:

AStA Advances in Statistical Analysis https://www.springer.com/journal/10182.
Epidemics https://www.sciencedirect.com/journal/epidemics.
Journal of Applied Statistics https://www.tandfonline.com/journals/cjas20 .
PLoS Computational Biology https://journals.plos.org/ploscompbiol/.
Scientific Reports https://www.nature.com/srep/.
Statistical Modelling https://journals.sagepub.com/home/smj.
Biostatistics https://academic.oup.com/biostatistics.

Teaching Bayesian Data Analysis II at Hasselt University.

Supervising PhD thesis of Bryan Sumalinab (Hasselt University): "Laplace approximations for modeling epidemic data".