“In God we trust, all others must bring data.” (W. Edwards Deming)

About Me

My research encompasses theoretical advances in statistical methods and machine learning, particularly at their intersection with finance, risk-based management, and predictive analytics. I emphasize the integration of theory and application, focusing on developing methodologies that not only advance statistical understanding but also provide practical solutions across diverse, interdisciplinary fields.

During my free time, I contribute to the open source community developing R packages. You can find more about me and my projects on my Resume and my GitHub.

Research Interest

My main interests are Quantitative Finance, Bayesian Methods, Computational Statistics, Deep Reinforcement Learning, Clustering, and Astrostatistics.

Main Publications

In chronological order:

  1. Quadros A., Higgins M., Silverstein B. (????) A Bayesian approach to generate distribution-based trading signals in pairs trading, Quantitative Finance (Submitted in Sep. 2024).

  2. Quadros A. (2024) mRpostman: An IMAP Client for R, Journal of Open Research Software, 12(1), p. 4. [link].

  3. von Borries G., Quadros A. (2022) ROC App: an application to understand ROC Curves. Brazilian Journal of Biometrics}, Volume 40, Issue 2. [link]

  4. Beaklini P., Quadros A., Avellar M., Dantas M., Cançado A. (2020) AGN dichotomy beyond radio loudness: a Gaussian Mixture Model analysis. Monthly Notices of the Royal Astronomical Society, Volume 497, Issue 2, September 2020, Pages 1463-1474. [link]

Education

[4] Ph.D. in Statistics (2021-present)
Kansas State University

[3] B.Sc. in Statistics (2018)
Recipient of the “Outstanding Student Award”
University of Brasilia (UnB)

[2] M.Sc. in Economic Development (2012)
State University of Campinas (Unicamp)

[1] B.Sc. in Geography (2008)
State University of Campinas (Unicamp)

References