I'm a Computational Sciences student at Minerva University working across data science, machine learning, statistical modeling, and software engineering. I like building projects that move beyond notebooks into clear analyses, reproducible pipelines, and usable data products.
An end-to-end public-health data product using official Brazilian surveillance data, time-aware backtesting, multi-horizon forecasting, uncertainty estimates, and an interactive Streamlit interface.
A personalized audio-ML study using 92,445 Spotify play events and 10-second song previews to compare handcrafted features, CNNs, Audio Spectrogram Transformers, listening context, and multimodal fusion. The key result: listening context explained substantially more skip behavior than audio alone.
Hierarchical Bayesian modeling of Argentine football attendance using PyMC, Negative Binomial models, posterior predictive checks, and PSIS-LOO model comparison.
A Python scheduling engine with a custom max-heap, task dependencies, fixed-time and bounded-window constraints, conflict detection, automated tests, and benchmark analysis through 1,000 tasks.
An algorithms project comparing greedy and global dynamic-programming approaches for reconstructing genealogical relationships from DNA sequences using Longest Common Subsequence similarity.
More projects across machine learning, statistics, algorithms, simulation, and data products.
Python R SQL PyTorch scikit-learn PyMC pandas NumPy Streamlit Git
I'm especially interested in work where data, modeling, and real-world product decisions meet.