I am a Data Scientist with a unique background in Cognitive Neuroscience. My approach to AI is deeply rooted in the study of complex systems: I don't just see rows of data; I see behavioral patterns.I treat data as a dynamic signal to be decoded and make it actionable.
Leveraging my background in Cognitive Neuroscience, I treat data as a dynamic signal with a rigorous experimental mindset:
- Behavioral Profiling: Treating machine telemetry as a proxy for system "health behavior."
- Pattern Recognition: Identifying hidden "rhythms" in noisy, high-volume datasets.
- Explainability (XAI): Making complex models transparent and actionable for human stakeholders.
- End-to-end ML pipelines From raw, noisy data preprocessing to modeling, tuning, explainability and interactive visualization
| Project | Challenge & Data | Tech Stack (Algorithms & Tools) | Impact & Knowledge Discovery |
|---|---|---|---|
| Predictive Maintenance for Urban Mobility | 7.5M Telemetry Logs. Transitioning gates from "fail-and-fix" to proactive maintenance. | ML: XGBoost, CatBoost, LightGBM, RF. DL/TS: LSTM, MiniRocket, sktime. XAI: SHAP. |
Built a Stakeholder Dashboard (Streamlit/Plotly). Isolated maintenance noise from operational stress for Root-Cause Analysis. |
| ECSS Compliance RAG Engine | Aerospace Standards (ECSS). Overcoming semantic ambiguity and deontic rigidity (SHALL/SHOULD) in complex regulatory frameworks. | Data Engine: NetworkX, ChromaDB. ML/LLM: LangChain, Hugging Face (Qwen2.5), Sentence-Transformers. Visuals: Streamlit, Pyvis. |
Developed a Hybrid RAG Pipeline merging semantic vector search with a Normative Document Graph. Built an interactive Sub-graph Explorer ensuring grounded retrieval and resolving topological normative cross-references. |
| Motor Imagery BCI: Decoding Neural Intentions | Brain-Computer Interface. Decoding motor intentions from noisy EEG signals (BCI Competition IV-2a). | Neuro-Proc: MNE-Python, ICA, CAR, Filtering (8-30Hz). ML: CSP (Spatial Filters), LDA. DL: EEGNet. Deployment: Streamlit, PyLSL. |
Developed a Real-Time BCI Simulation demos 2-class and 4 class. Visualized Neural Topomaps (C3/C4) to validate spatial feature extraction and ensure Signal-to-Noise (SNR) optimization. Designed a robust interpretable models (CSP + LDA) and deep learning (EEGNet) pipelines for subject-independent EEG decoding. |
| Alzheimer’s Clinical Support System | Medical Diagnostics. Early detection of dementia using clinical and lifestyle datasets. Addressing class imbalance and demographic biases. | ML: LightGBM (Baseline vs Fin-etuned), CatBoost, XGBoost, Random Forest. XAI: SHAP (Waterfall Plots). Visuals: Streamlit, Plotly (Radar Charts). |
Developed a Diagnostic Support Dashboard. Prioritized Clinical Integrity by selecting baseline LightGBM over fine-tuned variant and reducing overfitting for superior generalizability. Visualized Patient Profile vs. Population Benchmarks to facilitate evidence-based clinical decisions. |
| Shelter Intelligence: Adoption Optimization | Social AI. Identifying demographic and intake factors influencing animal adoptability. | Clustering: K-Means,DBSCAN,Hierarchical . Classification and Regression: Random Forest, Decision Trees, KNN, SVM, LinearRegression. |
Analyzed factors impacting stay duration and adoption outcomes to optimize resource allocation in shelters. |
| NeuroPsy-Research Archive | Behavioral Data Modeling. Mapping clinical traits to cognitive and perceptual performance and body perception. | PsyToolkit, SPSS, Excel. Paradigms: Posner Task, Memory Task, Shelf Task, ECR- 12, AST, STA-Y2,LCSQ, BDS |
Engineered automated scoring for clinical scales and biometrics. Validated "Healthy is Up" metaphor via spatial categorization and BMI correlations. Mapped attachment style-driven biases in attention (Posner), memory (Old-New), and social interpretation (AST) |
- Master in Big Data Analytics and AI for Society | University of Pisa
- MSc in Cognitive Neuroscience and Clinical Neuropsychology | University of Campania
"Treating data not just as numbers, but as the behavior of a complex system."