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
(he/him)
PhD Student in Artificial Intelligence
Professional Summary
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.
Education
PhD in Artificial Intelligence
2025-11-01
Scuola Superiore Sant'Anna & University of Pisa
Second-Level Master in Customer Experience, Statistics, ML and AI
2025-02-03
2026-02-02
University of Rome Tor Vergata
MSc in Financial Markets and Financial Intermediaries
2022-10-20
2025-04-04
University of Rome Tor Vergata
BSc in Governance & International Relations
2019-09-15
2022-09-30
University of Rome Tor Vergata
BA in Philosophy
2014-09-01
2017-07-01
University of Rome La Sapienza
Interests
Agent-Based Modeling
Multi-Agent Systems
Economic Simulation
Statistical Model Checking
Robust Statistics
Financial Econometrics
Complex Networks
📚 Research Overview
I study how adaptive agents make decisions, interact through changing networks, and generate aggregate economic dynamics.
My work combines AI, agent-based modeling, and quantitative methods to build economic simulations that are not only more flexible, but also more testable and empirically grounded.
Core Research Pillars:
- Adaptive Multi-Agent Systems: Designing agents that switch across heuristic, learned, and deliberative policies while their interaction networks evolve endogenously.
- Statistical Verification of Simulations: Using statistical model checking to evaluate agent-based models through explicit properties, parameter exploration, and convergence diagnostics.
- Robust Quantitative Methods: Applying robust statistics, network methods, and econometric tools to problems in systemic risk, portfolio allocation, and firm-level distress.
Latest News
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.
Publications
(2026).
Statistical model checking of the Island Model: an established economic agent-based model of endogenous growth.
MARS 2026.
(2026).
A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics.
JADT 2026 (in review).
(2026).
Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets.
Master Thesis.
(2025).
High-dimensional Robust Portfolio Optimization Under Contamination: A Factor-Analytic Approach.
Computational Economics (in review).