Über Leonardo
I hold a PhD in Data Science with a strong quantitative background in applied mathematics and physics. I specialize in time series analysis, statistical modeling, and graph theory, with applications to social systems, interaction data, and complex relational datasets (e.g. social networks, behavioral and communication data).
I have extensive experience working with large-scale datasets, designing end-to-end data pipelines: data extraction (SQL), cleaning, transformation, exploratory analysis, modeling, and validation. I routinely handle structured and time-dependent data, focusing on robustness, interpretability, and reproducibility of results.
On the technical side, I develop custom Python tools and libraries for data analysis and graph-based modeling, including simulations, metrics, and model evaluation. My workflow relies on Python (pandas, NumPy, SciPy, scikit-learn), SQL databases, and well-structured codebases suitable for production or research-grade environments.
Through my doctoral training, I have developed strong skills in problem formulation, complex system analysis, and quantitative reasoning, allowing me to tackle ill-defined business or analytical problems and translate them into solvable data-driven solutions. I am comfortable working at the interface between theory and practice, and delivering clear, actionable insights to technical teams and decision-makers.
Italienisch
Muttersprachlich oder zweisprachig
Englisch
Verhandlungssicher
Spanisch
Verhandlungssicher
Französisch
Konversationssicher
Deutsch
Grundkenntnisse
Vor Ort möglich
Barcelona (bis zu 50 km)
Projekt- und Berufserfahrung
- Aix–Marseille UniversitèPostdoctoral ResearcherFORSCHUNGJanuar 2025 - Heute (1 Jahr und 5 Monate)Marseille, France• • Built analytical pipelines for high-dimensional multivariate time series (MEG/fMRI) integrating statistical modelling, information theory, differentiable computation, and network analysis.• • Developed custom JAX routines (j it, vmap, vectorised transformations) for temporal modelling and integration with FRiTES/HOI.• • Designed algorithms for network dynamics, dynamical-systems inference, and predictive modelling.• • Worked with heterogeneous large-scale datasets under tight computational budgets.• • Led interdisciplinary collaborations across neuroscience, physics, and data science.
- Central European UniversityTeaching Assistant – Statistics & Data ScienceJanuar 2019 - Januar 2024 (5 Jahre)Vienna, ÖsterreichSupported undergraduate and graduate courses in Data Science and Statistics. Delivered tutorials and hands-on sessions covering probability and statistics, data analysis, and core machine learning concepts (supervised and unsupervised methods). Assisted students with Python-based data workflows, including data preprocessing, exploratory data analysis, and basic model evaluation. Contributed to exam preparation, grading, and one-to-one academic support.
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Ausbildung und Abschlüsse
- Ph.D.Ph.D.
- Ph.D. in Network & Data ScienceCentral European University2025Ph.D. in Network & Data Science