Python | Stefano Blando

Multi-Agent Orchestration

Wed, 18 Mar 2026 00:00:00 +0000

This project can be read as a system for real-time economic coordination under changing phases, partial information, and hard timing constraints.

The system is structured as an event-driven multi-agent orchestration layer. It listens to the game through SSE events, keeps a runtime representation of the environment, and switches strategy depending on the current phase of play: policy update, procurement auction, inventory reconciliation, and fulfillment.

What makes the project interesting is not just the use of multiple agents, but the way orchestration is tied to operational robustness. Different capability sets are activated at different stages, while local persistence, metrics, and replay analysis help make decisions more stable under noisy runtime conditions.

In practice, it is a good example of a project where agentic design had to be grounded in execution reliability rather than in generic prompting alone. The deeper pattern is auction-based coordination with inventory constraints and demand handling.

Interactive Explorer

The interactive app focuses on the engineering core of the project: the live event loop, shared runtime state, stylized agent interaction, phase-specific agents, capability partitioning, bounded memory, and KPI replay.

It is intentionally presented as a runtime architecture explorer, not as a fake benchmark dashboard. The goal is to make the orchestration logic legible in the same visual language used for the Island Model and PFSE apps, with emphasis on coordination, auctions, and fulfillment.

PokéNexus

Wed, 04 Feb 2026 00:00:00 +0000

PokéNexus was born from something very simple: I have loved Pokémon since I was a child, and at some point I wanted to merge that world with the kind of tools and ideas I now enjoy building with.

The result is a Streamlit app that turns the Pokémon universe into an interactive space where graph visualization, game mechanics, and API-driven data come together. I used NetworkX, PyVis, and Plotly to explore relationships between types and entities, while PokeAPI provides the live data layer for creatures, evolutions, and related information.

Instead of being just a static graph, the project grew into a small playable system: teams can be managed, items stored, badges collected, and different progression loops layered on top of the visualization. That mix of structure, exploration, and play is exactly what made the project fun to build.

It is not a research project, and it is not meant to be. It is a personal experiment where a childhood passion meets the way I now think about networks, interfaces, and interactive Python applications.

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

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

Advanced Recommender System

Fri, 20 Jun 2025 00:00:00 +0000

This project was developed within the CESMA Master’s program in collaboration with TIM. Instead of framing the problem as a standard classification task, the system was designed as a learning-to-rank pipeline for next-best-action recommendation.

That shift in framing matters because ranking is closer to the actual business decision: not just whether an action is good or bad, but which action should come first for a given user.

The pipeline combines careful validation, Bayesian optimization, and ensemble ranking strategies. The end result is a substantial improvement over baseline performance on NDCG@5, making the project a solid example of applied machine learning under realistic evaluation constraints.

Performance summary:

Stage	NDCG@5 Score	Improvement vs Baseline
Baseline Model	0.5030	–
Best Single Model	0.6838	+35.94%
Best Ensemble	0.6852	+36.23%

Overall, it is one of the clearest examples in the portfolio of taking a familiar ML task and reformulating it in a way that is better aligned with the actual decision problem.

Gas Network Risk Forecasting

Fri, 01 Nov 2024 00:00:00 +0000

This project was developed for the Hera Group Hackathon, where it earned 2nd place. The task was to detect gas leak risk in a setting dominated by imbalance, sparse events, and operational uncertainty.

The pipeline combines geospatial-temporal feature engineering with synthetic data augmentation through CTGAN and TimeGAN, then uses SHAP to keep the final model interpretable rather than purely predictive.

What I still like about this project is its balance between pragmatism and method: it is a hackathon project, but it already reflects an approach I use often, namely trying to make difficult prediction problems more robust without giving up explainability.

Custom Chatbot with RAG

Thu, 15 Feb 2024 00:00:00 +0000

This project uses a deliberately small but structured domain to explore a larger idea: how to make language-model outputs more reliable by grounding them in retrieved context.

The chatbot is built around a curated dataset of fictional characters and uses a full RAG pipeline with embeddings, retrieval, and prompt conditioning. The dataset is playful, but the methodological point is serious: retrieval changes the behavior of the model from generic completion to context-bounded reasoning.

Because the underlying data is semantically rich, the system can handle not only question answering but also character comparison, recommendation, and trait-based exploration. That makes it a useful compact example of retrieval-driven NLP design.