My research interests lie at the intersection of methodology, statistical innovation, and clinical psychology, with a particular focus on Multiverse Analysis and the development of advanced statistical approaches for psychological science. I am especially interested in how methodological rigor and transparent analytical frameworks can enhance the reliability and replicability of findings in clinical and applied psychology.

I completed my Bachelor’s degree in Scienze e Tecniche Psicologiche and my Master’s degree in Psicologia Clinica dello Sviluppo at the University of Padova, under the supervision of Prof. Gianmarco Altoè and Dr. Filippo Gambarota. My Master’s thesis bridges clinical psychology and quantitative methodology, applying Post-Selection Inference for Multiverse Meta-Analysis (PIMMA) to psychotherapy research on depression. This project has allowed me to explore how inferential multiverse approaches can be used to assess the robustness of clinical findings and better understand how analytical decisions shape conclusions.

From the beginning of my academic path, I have been drawn to the scientific foundations of psychology. I view psychology as a developing and inherently complex discipline, one that must navigate constructs that are often intangible and context-dependent. This complexity inspired my deep interest in research methodology, measurement validity, and reproducible data analysis. I see methodological reform as a necessary evolution of psychological science — one that demands both technical precision and philosophical reflection on how we produce knowledge.

My research philosophy is grounded in the belief that science advances cumulatively: observation and theory-building must be followed by rigorous testing, refinement, and falsification. I am particularly motivated by challenges related to theory construction, model specification, and bias reduction in empirical research. My clinical training has also informed this methodological orientation, as it has shown me the practical consequences of flawed or underpowered research on real-world interventions. This interplay between clinical insight and methodological innovation defines my broader scientific identity.

I am an active collaborator with the Psicostat research group (see: https://psicostat.dpss.psy.unipd.it/) at the University of Padova — a collective dedicated to improving the credibility and transparency of psychological research. Within this group, I have contributed to a departmental initiative aimed at establishing a departmental Methodological Review Board, designed to promote higher standards of rigor and reproducibility in research practices. These experiences have strengthened both my statistical and collaborative skills and reinforced my commitment to fostering open and cumulative science.

My research activities are primarily quantitative and computational, involving programming, statistical modeling, and simulation studies. However, my background in clinical psychology provides a valuable applied perspective: I am especially interested in how methodological advances can improve the validity of psychotherapy research and contribute to more robust, evidence-based clinical practice.

Beyond my doctoral project, my broader interests include meta-science, open science practices, theory evaluation, and statistical education. I am also interested in the philosophy of science, particularly the epistemological assumptions that underlie psychological theory and inference. On a personal level, I am strongly committed to research ethics, collaborative work, and Open Science, and I see transparency as core scientific virtue.