Side Quest | Stefano Blando

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.

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.

Real Estate AI Agent

Wed, 05 Mar 2025 00:00:00 +0000

This project explores how an AI agent can be used as a practical layer on top of a more traditional predictive workflow. In this case, the domain is real estate: price estimation, market analysis, and user interaction around property-related questions.

The project combines valuation models with an LLM-based interface for natural-language interaction and task-level orchestration. The interesting part is not just the agent wrapper itself, but the attempt to connect predictive models with a more usable front-end for exploration and decision support.

Lightweight Fine-Tuning with PEFT & LoRA

Wed, 20 Nov 2024 00:00:00 +0000

This project focuses on a practical question in NLP: how much useful adaptation can you obtain from a pretrained model without paying the full cost of fine-tuning everything?

Using LoRA on distilbert-base-uncased for sentiment analysis, the pipeline shows that a very small trainable subset of parameters can still deliver a strong performance jump over the zero-shot baseline. That makes the project less about squeezing out maximum benchmark accuracy and more about understanding the trade-off between performance and efficiency.

Built with the Hugging Face ecosystem, the implementation covers evaluation, LoRA configuration, training, and inference in a lightweight setup that remains accessible on modest hardware.

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.

AI Photo Editor with SAM & SDXL

Sun, 10 Mar 2024 00:00:00 +0000

This project explores the intersection of precise computer vision and generative image editing by combining Segment Anything (SAM) with Stable Diffusion XL.

The core idea is straightforward: segmentation provides exact control over what should be changed, while diffusion-based inpainting provides the generative flexibility to actually change it. That makes the system useful not only as a demo, but as a concrete example of how discriminative and generative models can be combined inside the same workflow.

Built in Python with PyTorch, Diffusers, and Gradio, the project supports interactive masking, object replacement, and background generation while keeping the pipeline lightweight enough to run on consumer hardware with the right optimizations.

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.