Fabio Demaria

Progetto di ricerca
Modern statistical learning techniques for high-dimensional datasets: implications for workplaces and sustainability

The advent of modern technologies has strongly contributed to the beginning of the information age, bringing large amount of data and new statistical-related challenges that to be addressed. Traditional statistical and econometric techniques such as linear models often work well in low-dimensional setting, but high-dimensional datasets require more powerful data manipulation tools like statistical learning techniques in the field of data mining. Such techniques consist of a set of tools for understanding complex datasets, and we can distinguish between supervised and unsupervised learning. In particular, modern statistical learning techniques will be implemented to explore different workers’ attitudes in the organizational context, as well as the sustainability performance of products within the LCA framework, with the ultimate goal to derive useful insights and develop metrics for both internal and external purposes.

Pubblicazioni

• Vacchi, M., Siligardi, C., Demaria, F., Cedillo-González, E., González-Sánchez, R., & Settembre-Blundo, D. (2021). Technological Sustainability or Sustainable Technology? A Multidimensional Vision of Sustainability in Manufacturing. Sustainability, 13(17), 9942. doi: 10.3390/su13179942