Network Science | Stefano Blando

RiskSentinel - Agentic Systemic Risk Simulator

Sun, 15 Mar 2026 00:00:00 +0000

RiskSentinel is an agentic systemic risk simulator built around a question I find both practical and conceptually interesting: if a large financial node is hit by a shock, how can we make the propagation of that stress visible, comparable, and explainable in real time?

Built for the Microsoft AI Dev Days Hackathon 2026, the project combines three contagion models, topology-aware analytics, and a bounded multi-agent workflow on top of research-grade market network data. The purpose is not only to simulate cascades, but to make them easier to inspect through natural-language interaction, interactive visualization, and model comparison.

What makes the project work is the combination of several layers that are often separated: a proper network simulation engine, an interface that makes the results explorable, and an agentic layer that helps interpret what is happening without pretending to replace the underlying quantitative machinery.

The result is a practical prototype for systemic risk monitoring, sitting between quantitative finance, complex systems, and AI-assisted decision support.

You can explore the project here:

Network Topology Analysis and Machine Learning Techniques for Systemic Risk Prediction in U.S. Equity Markets

Thu, 15 Jan 2026 00:00:00 +0000

Network Topology Analysis for Systemic Risk Prediction

Sat, 10 Jan 2026 00:00:00 +0000

This project corresponds to my CESMA thesis on systemic risk prediction in U.S. equity markets. The underlying question is simple but important: can network topology say something useful about market stress before standard indicators do?

Using daily data from 210 S&P 500 constituents (2013-2025), the project combines dynamic correlation networks with machine learning models ranging from gradient boosting to Graph Neural Networks such as GraphSAGE and GAT. The goal is not just classification accuracy, but economic usefulness under realistic validation and backtesting constraints.

The most interesting result is that network-derived signals appear to carry genuine early-warning information, especially around severe and structurally fragile market states. In the strongest configurations, the framework improves both timing and trading performance relative to simpler baselines.

This project is paired with the related thesis page, where the same work is presented as a publication entry.

NLP & Semantic Network Analysis

Sat, 10 Jan 2026 00:00:00 +0000

This project is the technical implementation behind the publication “A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics” (JADT 2026, in review). It operationalizes the full analytical workflow used in the paper, from data preparation to cross-method validation and result comparison.

The pipeline combines R and Python modules over a large multilingual review corpus, including: preprocessing and TF-IDF, LDA topic modeling, LSA and Correspondence Analysis, lexicon- and model-based sentiment analysis, clustering, and co-occurrence network analysis. The repository also includes cross-platform validation scripts to compare method outputs and check structural stability across implementations.

The central objective is methodological robustness: verifying which findings remain consistent when methods, model families, and language-specific components vary. In this sense, the project is not a generic NLP demo, but a reproducible research pipeline designed for quantitative validation of text-analytic conclusions.