🎓 MRes in Economics & Decision Sciences — HEC Paris
🎓 MA in Economic Theory — ITAM Mexico
🎓 BSc in Economic Sciences — University of Brasília

Quantitative researcher working at the intersection of statistical physics, economic theory, and machine learning. Heavy-tail modeling, extreme value theory, and multi-agent learning dynamics. 4 peer-reviewed publications (150+ citations). Currently seeking roles as a Quantitative Researcher, Data Scientist, or ML Engineer.

• Truncated Lévy Flights: Heavy-tail return modeling connecting bounded empirical distributions with power-law tails; basis for two published papers in Physica A and Chaos, Solitons & Fractals.
• Correlated Q-Learning: Multi-agent learning algorithm connecting correlated equilibrium, quantal response, and algorithmic stability.
• ML vs Decision Theory: Benchmarking ML models against structural economic models (CPT, EUT) on behavioral data.

• Bypassing the truncation problem of truncated Lévy flights — Physica A, 2020. Power-law tail model for financial return distributions.
• Retrodicting with the truncated Lévy flight — Chaos, Solitons & Fractals, 2022. Connects bounded historical distributions with unbounded future tails.
• The duration of historical pandemics — Chaos, Solitons & Fractals, 2022. Extreme value theory applied to pandemic duration modeling.
• An empirical overview of nonlinearity and overfitting in machine learning using COVID-19 data — Chaos, Solitons & Fractals, 2020. SVR, hyperparameter search, and interpolation on epidemiological data.

Methods: Extreme Value Theory · Lévy Processes · GARCH/EGARCH · Causal Inference · Multi-Agent RL · Mechanism Design
Tools: Python (PyTorch · XGBoost · LightGBM · scikit-learn) · R · SQL · C
Languages: Japanese (JLPT N1) · English · Mandarin (HSK 5) · Portuguese · Spanish

🌐 Portfolio · LinkedIn · Google Scholar