Awarded June 2026 to Dr. Thomas Olino
This award recognizes a faculty member who has demonstrated exceptional commitment to mentorship within the Quant Family Collective. Through your generosity in sharing expertise, fostering supportive learning environments, encouraging the growth of emerging researchers, and advancing accessible and inclusive quantitative education, you have made an enduring contribution to our community.
Dr. Thomas Olino, director of the Child and Adolescent Development of Emotion, Personality and Psychopathology Lab at Temple Univeristy, is trained as a clinical psychologist with research interests in developmental psychopathology, particularly on the development of depression and anxiety. As part of this substantive line of research, his questions required quantitative training in longitudinal modeling and approaches to understand measurement across informants and time. He now teaches courses in multivariate methods and structural equation modeling, in addition to providing consultation to students and colleagues at his home and other institutions using an array of methods.
Awarded June 2026 to Gabriele Limonta
This award is presented to an early-career trainee who has demonstrated exceptional engagement in the Quant Family Collective through active participation, intellectual curiosity, and meaningful contributions to the community. Your willingness to share your work, support fellow members through feedback and resource-sharing, and contribute to an inclusive and collaborative learning environment has made a significant impact on QFC.
Gabriele Limonta is currently a pre-doctoral research fellow at the University of Milano-Bicocca, where his research focuses on psychometrics, personality psychology, research methodology, and open science. He is developing the Psychogeometric Hypothesis, a novel framework for investigating how people perceive and organize personality through judgments of interpersonal similarity. This approach complements traditional psycholexical models by integrating multidimensional scaling, permutation-based methods, mixed-effects modeling, generalized Procrustes analysis, and natural language processing. In parallel, he is also developing a blockchain-based framework to support open science practices by transforming research outputs into transparent, durable, and easily auditable records, while enabling novel systems for recognizing scholarly contributions through cryptocurrencies and non-fungible tokens. As an early-career researcher, he aspires to contribute to the development of a more rigorous, reproducible, open, and cumulative psychological science by advancing both methodological innovation and scientific transparency.