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Lidia Scudiero

Hi, My name is Lidia Scudiero

Data Scientist and Analyst with a background in Cognitive Neuroscience and Big Data Analytics. Transforming data into behavioral insights to build interpretable AI and intelligent systems.

About Me Treating data not just as numbers, but as the behavior of a complex system

Get to know me!

I am a Data Scientist with a background in Cognitive Neuroscience and Big Data Analytics.

With a rigorous experimental mindset, I build end-to-end ML pipelines—from raw data preprocessing to fine-tuned and explainable modeling (XAI) and interactive visualization. My hands-on expertise includes Predictive Maintenance for IoT, Time-Series Analysis, and advanced NLP techniques like Retrieval-Augmented Generation (RAG) and Sentiment Analysis.

I excel at bridging the gap between technical teams and stakeholders by translating complex analytical results into interactive dashboards and data-driven reports. I am driven to apply data-centric solutions across healthtech, intelligent systems, behavioral analytics and applied AI for complex real-world data.

My Skills

Programming & Core

Python
R
SQL
Pandas
NumPy
SciPy

ML, AI & NLP

Scikit-Learn
Keras
TensorFlow
sktime
SpaCy
NLTK
Explainable AI (SHAP)

Generative AI

Hugging Face
Mistral
Cohere
RAG

Neuroscience

MNE-Python
PyLSL
LSL
BCI

Visualization & BI

Streamlit
Plotly
Altair
Matplotlib
Seaborn
DuckDB
Excel
SPSS

Geospatial & Networks

Folium
NetworkX
OSMnx
Shapely
scikit-mobility

Collaboration, Web and Data Crawling & Tools

Git
Docker
VS Code
Selenium
BeautifulSoup
Notion
Canva
Inkscape
PsyToolkit

Projects Exploring complex systems from urban infrastructure and brain activity to healthcare decision support.

Software Screenshot

Predictive Maintenance for Urban Mobility

How do you predict failures in a system with 7.5M noisy logs and no dedicated sensors? A feasibility study on transforming urban mobility maintenance into a predictive system under strict NDA constraints.

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Software Screenshot

Motor Imagery BCI: Decoding Neural Intentions

Decoding motor imagery (left vs right hand, 4-class extension) from noisy EEG signals using CSP + LDA and EEGNet, with real-time BCI simulation demos and spatial validation of motor cortex activity.

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Software Screenshot

Alzheimer’s Clinical Support System

Explainable AI system for Alzheimer’s risk prediction using ensemble machine learning models, with SHAP-based interpretability and an interactive Streamlit dashboard for clinical decision support.

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Contact Let’s connect and turn data into meaningful decisions.