In October 2018 Valeria Aloisi obtained a Bachelor’s degree, cum laude, in Information Engineering at the University of Salento. She specialized by getting a Master’s degree, cum laude, in Computer Engineering at the University of Salento in October 2020, presenting a thesis titled “Exposure to air pollution and Covid-19 spread in Italy: a regional cross-sectional study”. This thesis consists of a nationwide epidemiological study, based on region-level resolution data, with the aim of assessing the potential association between the long-term average concentration of PM2.5 and the number of deaths due to Covid-19, by taking confounding factors into account.
She has been collaborating with the Advanced Scientific Computing (ASC) division of the Euro- Mediterranean Center on Climate Change (CMCC) since November 2020.
Her main skills include good knowledge of programming languages and optimal knowledge of machine learning tools and techniques (e.g., univariate and multivariate regression analysis with linear, polynomial, Poisson, ZIP, ZINB, Negative Binomial, or Logistic regression model).
Her research interests concern the application of Machine learning algorithms to epidemic phenomena like the Covid-19 pandemic.
- An Artificial Neural Network-based approach for predicting the COVID-19 daily effective reproduction number Rt in Italy
- MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain
- The effect of known and unknown confounders on the relationship between air pollution and Covid-19 mortality in Italy: A sensitivity analysis of an ecological study based on the E-value
- Limits of Compartmental Models and New Opportunities for Machine Learning: A Case Study to Forecast the Second Wave of COVID-19 Hospitalizations in Lombardy, Italy