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stefano-blando/README.md

Stefano Blando

Website LinkedIn ORCID Email

PhD Candidate in Artificial Intelligence | AI, Agent-Based Modeling, and Economics

PhD Candidate in the National PhD Program in Artificial Intelligence at Scuola Superiore Sant'Anna and the University of Pisa. My research lies at the intersection of AI, agent-based modeling, and economics, with a focus on adaptive multi-agent systems, statistical verification of simulations, and robust quantitative methods for financial and socio-economic data.

Core Research: adaptive multi-agent systems, economic simulation, statistical model checking, robust statistics, financial econometrics.

Current Research

PhD Project: Learning How to Learn — Adaptive Cognitive Architectures for Economic Network Formation
I study how economic agents can switch across heuristic, learned, and deliberative policies while their interaction networks evolve endogenously.

Current lines of work

  • adaptive multi-agent architectures for economic simulation
  • statistical verification of agent-based models through explicit properties and convergence diagnostics
  • robust quantitative methods for portfolio allocation, systemic risk, and firm-level distress

Featured Projects

  • Risk Sentinel: An interactive network systemic risk contagion model and agentic AI workflow for stock market networks. (GitHub Repository)
  • Island Model SMC: A parallel Sequential Monte Carlo algorithm for the automated parameter calibration and statistical validation of complex dynamical stochastic simulation models. (GitHub Repository)
  • Multi-Agent Orchestration: A framework designed for the orchestration, scheduling, and formal verification of complex multi-agent system workflows. (GitHub Repository)

Recent Awards & Recognition

  • VADISTAT Award for Best Scientific Contribution (JADT 2026, Palermo)
    Presented by the Associazione VADISTAT - Per Simona Balbi for the paper "A Multi-Method Validation Framework for Large-Scale Multilingual Text Analytics".
  • 2nd Place - AI Data Hackathon 2024
    For developing robust financial machine learning modeling under tight time constraints.
  • Academic Tutorship Awards (x2, University of Rome Tor Vergata)
    Awarded for excellence in teaching assistance and student academic support.

Tech Stack & Toolkit

  • Languages: Python, MATLAB, R, SQL, SAS, Bash
  • AI Frameworks & Platforms: Claude Code, Codex, Antigravity, PyTorch, JAX, Streamlit
  • Academic Writing & Workflows: Git, LaTeX, Bookdown, Markdown

Pinned Loading

  1. systemic-risk-prediction systemic-risk-prediction Public

    Network Topology Analysis & Machine Learning for Systemic Risk Prediction in Equity Markets

    Jupyter Notebook

  2. real-estate-ai-agent real-estate-ai-agent Public

    Real Estate AI Agent: Multi-Agent Simulation & Automated Valuation

    Jupyter Notebook

  3. advanced-recommender-system advanced-recommender-system Public

    Advanced Recommender System with Collaborative Filtering & Deep Learning

    Python

  4. multi-agent-orchestration multi-agent-orchestration Public

    Multi-Agent Orchestration & Reinforcement Learning Simulation

    Python

  5. nlp-semantic-network nlp-semantic-network Public

    NLP & Semantic Network Analysis for Multilingual Text Analytics

    Jupyter Notebook

  6. peft-model-finetuning peft-model-finetuning Public

    Parameter-Efficient Fine-Tuning (PEFT) for Large Language Models

    Jupyter Notebook