I am Associate Professor of Statistics for social sciences in the Department of Statistical Sciences at the University of Padova and I am affiliated with the GNCS research group of INdAM.

Much of my work focuses on how to model imprecise and coarsened measurements in social and behavioral data, with a longstanding interest in fuzzy statistical methods. I study probabilistic and Bayesian approaches to non-stochastic measurement error, treating fuzzy and related representations as one way in which coarsening processes manifest in observed data. A second line of research concerns latent structure estimation, including models that decouple measurement information in both structured survey responses and unstructured inputs. I am particularly interested in Bayesian methodologies for complex social and behavioral data, ranging from approximate to computationally intensive inferential approaches. More recently, I have extended these interests to text and network data, with the aim of integrating heterogeneous sources and uncovering rich patterns in social phenomena.

I completed my doctoral studies at the University of Trento and KU Leuven, combining psychometrics with statistical modeling for behavioral sciences, and later held a postdoctoral position at the Wigner Research Centre for Physics (MTA) in Budapest.

In my research practice, I am guided by the principles of Slow Science.