Theory and results for creating robust importance sampling estimators via winsorization, with finite-sample optimality guarantees.

Results and award-winning methods of a year-long data challenge to predict the weather 2-6 weeks in advance.

How to scale one of the main Bayesian models for sparse high-dimensional regression to hundreds of thousands of predictors.

Using Markov chains to decode ciphered messages written in graffiti across the walls of Rio de Janeiro.


As an instructor at Stanford University:

  • STATS302: Qualifying Exams Workshop (Probability Theory). Summer 2017.

As a teaching assistant at Stanford University:

  • STATS315B: Modern Applied Statistics II (Graduate). Spring 2018.
  • STATS370: Bayesian Statistics (Graduate). Winter 2018.
  • STATS202: Data Mining and Analysis (Graduate). Fall 2015 and 2017.
  • MATH230B: Theory of Probability II (Graduate). Winter 2017.
  • MATH230A: Theory of Probability I (Graduate). Fall 2016.
  • STATS216: Statistical Learning (Graduate). Fall 2014, Summer 2015 and 2016.
  • STATS160: Statistical Methods (Undergraduate). Spring 2016.
  • STATS200: Statistical Inference (Graduate). Winter 2016.

As a teaching assistant at PUC-Rio:

  • MAT2621: Measure Theory and Integration (Graduate). Spring 2012.
  • ECO1215: Game Theory (Graduate). Fall 2011.
  • ECO 1113: Microeconomic Theory I (Undergraduate). Winter 2010.
  • ECO1721: Statistics and Econometrics (Undergraduate). Fall 2010.