Feliciani Thomas

Assegnista

Thomas Feliciani is a research fellow at Politecnico di Milano and computational social scientist specializing in science policy and peer review.

He earned his PhD in sociology from the University of Groningen, where he applied opinion dynamics to explore the social processes linking residential segregation and opinion polarization.

Alongside his PhD and during his postdoctoral research—first at University College Dublin and now at Politecnico di Milano—Thomas has focused on grant peer review, investigating the factors influencing funding recommendations by reviewers and the deliberations of review panels.

His work aims to identify policy interventions that can improve the quality and equity of funding decisions.

Carriera

PHD: Department of Sociology / ICS, University of Groningen, Netherlands

Ricerca

Computational social science; social simulation; science of science; peer review; opinion dynamics

Pubblicazioni Selezionate

Feliciani, T. (2025). Divided Spaces and Divided Opinions: Modeling the Impact of Residential Segregation on Opinion Polarization, University of Groningen. https://doi.org/10.33612/diss.1161823386

Feliciani, T., Luo, J., & Shankar, K. (2024). Funding lotteries for research grant allocation: An extended taxonomy and evaluation of their fairness. Research Evaluation, 33(1), rvae025. https://doi.org/10.1093/reseval/rvae025

Feliciani, T., Tolsma, J. & Flache, A. (2023). Ethnic segregation and spatial patterns of attitudes: studying the link using register data and social simulation. Journal of Computational Social Science. https://doi.org/10.1007/s42001-023-00216-9

Feliciani, T., Luo, J., & Shankar, K. (2022). Peer reviewer topic choice and its impact on interrater reliability: A mixed-method study. Quantitative Science Studies, 3(3), 832–856. https://doi.org/10.1162/qss_a_00207

Feliciani, T., Morreau, M., Luo, J., Lucas, P., & Shankar, K. (2022). Designing grant-review panels for better funding decisions: Lessons from an empirically calibrated simulation model. Research Policy, 51(4), 104467. https://doi.org/10.1016/j.respol.2021.104467