Projects

Estimating Data Centres EU
Estimating the Energy Consumption and Environmental Impacts of Data Centres in the EU

Master’s thesis: Developed a bottom-up, area-based model to estimate the energy consumption and CO₂ emissions in EU data centers. Included geospatial clustering, scenario analysis, and policy implications.

Python (Pandas, NumPy, Scikit-learn) Web Scraping (Selenium) Geocoding (Nominatim, Photon) Machine Learning (Random Forest, TF-IDF) Spatial Analysis & Visualization
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Police Lethality Sao Paulo
Not All Roofs Are Equal: Land Use, Migration, and Environmental Inequality across Germany

Geospatial analysis for #DataFestGermany2025 at Ludwig-Maximilians-Universität München. We explored how spatial inequality and migration intersect in housing access across Germany, visualizing disparities in living space and their links to income, regional divides, and environmental variables. This project won the Best Visualization prize.

R (ggplot, sf, tidyverse) Feature Importance Geospatial Regression
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AI Act Environmental Risks
Identifying AI’s Environmental Risks: Using NLP to Analyzing Public Feedback on the AI Act

Used Natural Language Processing to analyze over 400 public consultation submissions on the EU AI Act, focusing on climate and environmental risks.

Python (Pandas, Numpy, Scikit-learn) NLP (NLTK, Word2Vec, Transformers) Clustering (UMAP, KMeans) Web Scraping (Selenium, PyPDF2) Visualization (Matplotlib, Seaborn, WordCloud)
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Police Lethality Sao Paulo
Geospatial Analysis of Police Lethality in São Paulo, Brazil

Spatial analysis and modeling of police-related deaths in São Paulo using census, income, and incident data. I was responsible for the data visualization and the spatial regression models.

R (sf, spdep, spatialreg, tidyverse, ggplot2) Spatial Regression (SAR/SEM) LISA Clustering Mapping & Visualization
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Employment Bias ML
Employment Bias: ML for Labour Market Discrimination

Developed and evaluated machine learning models to predict self-reported job discrimination among immigrants and minorities across Europe, using the EU-MIDIS II survey. I was responsible for building, tuning, and interpreting the ML classification models.

Python (pandas, numpy, Matplotlib & Seaborn) scikit-learn XGBoost Cross-Validation & Grid Search
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Debt Settlement App
Debt Settlement App

Full-stack web app for tracking and settling debts between users. Built for Data Structures & Algorithms (2024), the app features a dynamic transaction network, debt tracking, and PayPal payment links. I led the frontend development and visualizations.

Python Flask HTML/CSS Bootstrap PayPal API Git
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Impunity in Intentional Murders and Feminicide, Impunidad Cero 2022
Impunity in Intentional Murders and Feminicide

I coordinated this national report analyzing impunity levels in intentional homicides and femicides in Mexico. Between 2016 and 2021, only 7 out of every 100 intentional homicides led to a conviction, and less than half of registered femicides were solved.

R (tidyverse, ggplot2, dplyr) Statistical Analysis Data Visualization Report Writing
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Impunity in Intentional Murders and Feminicide, Impunidad Cero 2022
Digital Transformation of the Public Prosecutors’ Offices in Mexico 2024

I led and coordinated this nationwide assessment of digital transformation across Mexico’s 32 state prosecutors’ offices. The project developed a methodology to measure progress in areas like digital platforms for complaints, case management systems, data-driven analysis, and cybersecurity.

R (tidyverse, ggplot2, dplyr) Methodology Design Policy Evaluation Report Writing
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5,013 Murders in Mexico City, México Evalúa 2018
5,013 Murders in Mexico City

In my first research collaboration as research assistant, I contributed to this report mapping homicide “hotspots” in Mexico City. The study identified priority zones for intervention and produced practical recommendations for targeted policing and urban policy.

Data Cleaning Geospatial Analysis ArcGIS/QGIS Geocoding
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