Presented at MARS @ ETAPS 2026
I presented our Island Model paper at MARS @ ETAPS 2026 in Turin. It was the first presentation of my first PhD paper and my first international conference.
Stefano Blando
PhD Student in Artificial Intelligence
Stefano Blando is a PhD student in the National PhD Program in Artificial Intelligence at
Scuola Superiore Sant’Anna and the University of Pisa. His research lies at the intersection
of AI, agent-based modeling, and economics. He studies adaptive multi-agent systems,
statistical verification of economic simulations, and robust quantitative methods for
financial and socio-economic data.
Statistical model checking of the Island Model: an established economic agent-based model of endogenous growth
Presented at MARS @ ETAPS 2026, this paper uses MultiVeStA to give the Island Model a more rigorous and reproducible statistical analysis.
Paper Accepted at MARS @ ETAPS 2026
Our paper on statistical model checking for the Island Model has been accepted at MARS @ ETAPS 2026. I will present it in Turin on April 12, 2026.
RiskSentinel for Microsoft AI Dev Days 2026
RiskSentinel is the project I built for Microsoft AI Dev Days Hackathon 2026: a multi-agent simulator for systemic risk, contagion analysis, and interactive stress testing.
Master Graduation
Graduation at University of Rome Tor Vergata with final grade 110/110 cum laude. Thesis on systemic risk prediction with network topology and machine learning.
Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets
Leveraging Graph Neural Networks (GNNs) and complex network theory to detect early warning signals of systemic risk in financial markets.
A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics
In review at JADT 2026, this paper validates 18 analytical approaches on 1M+ texts to identify method-invariant findings.
High-dimensional Robust Portfolio Optimization Under Contamination: A Factor-Analytic Approach
Based on my MSc thesis, this paper is currently in review at Computational Economics and develops a robust framework for portfolio optimization under contamination.
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