Computer Engineering student at FEUP with a strong practical focus on Python, machine learning, data engineering and quantitative research.
I enjoy building end-to-end technical systems, from data acquisition and validation to modelling, experimentation and reproducible evaluation.
Geometry-aware medical-imaging research pipeline for 4,407 knee MRI studies and 12 diagnostic targets, including DICOM geometry, malformed-image handling, GPU preprocessing and deterministic study-level representations.
Public technical showcase: github.com/Tomashcv/RSNAKnee2026-Showcase
Paper/demo-first deterministic execution engine with durable state, broker reconciliation, duplicate prevention and crash recovery.
Fail-closed market-data acquisition and causal research platform for 12.3M+ audited NQ/MNQ futures records.
Reproducible research into multidimensional purchasing power, survey-weighted economic coordinates and empirical state geometry.
TypeScript/React Native sports social platform with Expo, Supabase, PostgreSQL, RLS/RPC boundaries and an append-only virtual ledger.
Leakage-aware football market research with point-in-time features, nested temporal validation, market baselines and paper-only tracking.
Python · pandas · NumPy · SQL · Machine Learning · XGBoost · PyArrow · Parquet
Git · Linux · Testing · REST APIs · Java · C · C++ · TypeScript
BSc in Informatics and Computing Engineering @ FEUP
Porto, Portugal
Seeking a 2026/27 curricular internship
LinkedIn: linkedin.com/in/tomas-vitorino
