Brad Rava’s research interests focus modern statistical methods for addressing pressing societal problems that arise from combining automated decision making with high-risk scenarios. To properly communicate uncertainty in these high-risk scenarios, Brad’s research has drawn upon Empirical Bayes techniques, Fairness in Machine Learning, Statistical Machine Learning, and High Dimensional Statistics.

The advancement of machine learning in the past decade has had profound impacts on society. Algorithms that were previously expensive and hard to implement have become accessible to the general public. As a consequence, automated decision systems have started being used in sensitive areas such as medical diagnosis, bail sentencing, and financial services. These application areas require a more nuanced view of uncertainty estimation since the consequences of making a mistake can directly impact peoples happiness and quality of life. When artificial intelligence is used to make decisions for many people, it is important that we rigorously control severe consequences and that we properly communicate the uncertainty associated with these mistakes to practitioners.

NSF Graduate Research Fellowship in Mathematical Statistics, USC’s Marshall Fellowship (given to the top 3 Ph.D. students at the business school), USC’s Global Branding Fellowship, and Correlation-One Southern California Datathon: 1st place prize.