I'm a data scientist with a background in economics, data and social sciences. Passionate about solving real-world problems with technology and creativity.
Python, R, SQL, MATLAB
Stata, SPSS, Excel, Tableau
Machine Learning: Random Forest, SVM, K-Means, Linear & Logistic
Regression
Deep Learning: PyTorch, TensorFlow, and Keras
Prompt Engineering: GPT, Gemini
Experienced in applying Hugging Face models for video analysis
HTML, CSS, JavaScript, Node.js, Supabase, MongoDB, RESTful APIs
GitHub, Overleaf, Hex, Jupyter, VS Code
Python, NLP, DeepSeek-R1, Ollama, Docker
A Dockerized DeepSeek-R1–based tool that generates high-quality synthetic datasets from question lists for NLP training, producing per-question CSVs with realistic short answers and descriptions. Supports both command-line and interactive modes, with robust error handling and model configuration options.
Python, NLP, spaCy
A Python tool that reads PDF resumes and uses spaCy to remove gender-specific language, making resumes more inclusive and neutral.
R, ACS Census Data, Spatial Analysis, Causal Inference
A data-driven exploration of housing affordability and mobility patterns in San Mateo County, uncovering how rent burden affects residential stability across income and immigrant communities.
Python, Machine Learning, Sovereign Bond Data, GDELT, Eurozone Markets
Built an unsupervised learning pipeline to detect short-term political risk using Italian bond spreads, news sentiment, and equity index performance. Used clustering and PCA to visualize shifts in investor perception following a major defense policy shock.
Relational Trust, Preferential Use, Party Media, Affective Perception — Selected by ICA 2023 and IAMCR 2023
Potential Mothers, Gender Discrimination, Second-Child Policy