Previously, she worked as data analyst and data scientist, acquiring expertise in data extraction, manipulation and analysis. Her research interests involve the use of latent variable models to assess unobservable constructs, data and text mining techniques applied to management of innovation data and, in general, the use of quantitative methods for qualitative data.
Irene received a Ph.D. in Statistical Sciences from the University of Bologna, and she has a strong background in statistics applied to several contexts and disciplines -- including management, economics, education, medicine and genetics, among others. Prior to her doctoral studies, Irene graduated in Economics (University of Pisa) and received a MSc. in Statistical Sciences and Economics (University of Bologna).
What are Irene’s plans for this new chapter of hes academic career, in connection with the objectives of EMbeDS?
"I will integrate different company-related data sources to analyse both determinants and outcomes of innovation processes occurring in small and medium-size enterprises. The aim of this exercise is to aid in the development of theoretical models of business dynamics and business strategies.”
“I will also develop statistical models to characterize and evaluate European innovation policies, with a special focus on Open Innovation practices.”