activity
20202025
most citedForecasting asylum-related migration flows with machine learning and data at scale

84 citations · 86 across the 4 of their papers we have counts for

collaborators

5 papers

stat.AP2025

Dynamic Attention (DynAttn): Interpretable High-Dimensional Spatio-Temporal Forecasting (with Application to Conflict Fatalities)

Stefano M. Iacus, Haodong Qi, Marcello Carammia +1

Forecasting conflict-related fatalities remains a central challenge in political science and policy analysis due to the sparse, bursty, and highly non-stationary nature of violence…

cs.CL2025

The Human Flourishing Geographic Index: A County-Level Dataset for the United States, 2013--2023

Stefano M. Iacus, Devika Jain, Andrea Nasuto +3

Quantifying human flourishing, a multidimensional construct including happiness, health, purpose, virtue, relationships, and financial stability, is critical for understanding soci…

cs.CL2024★ 2 cited

Rethinking Scale: The Efficacy of Fine-Tuned Open-Source LLMs in Large-Scale Reproducible Social Science Research

Marcello Carammia, Stefano Maria Iacus, Giuseppe Porro

Large Language Models (LLMs) are distinguished by their architecture, which dictates their parameter size and performance capabilities. Social scientists have increasingly adopted…

stat.AP2022

Forecasting Change in Conflict Fatalities with Dynamic Elastic Net

Fulvio Attinà, Marcello Carammia, Stefano Maria Iacus

This article illustrates an approach to forecasting change in conflict fatalities designed to address the complexity of the drivers and processes of armed conflicts. The design of…

stat.AP2020★ 84 cited

Forecasting asylum-related migration flows with machine learning and data at scale

Marcello Carammia, Stefano Maria Iacus, Teddy Wilkin

The effects of the so-called "refugee crisis" of 2015-16 continue to dominate the political agenda in Europe. Migration flows were sudden and unexpected, leaving governments unprep…