Bradley Rava

Brad Rava is a Lecturer in the discipline of Business Analytics at the University of Sydney's Business School. His research focuses on Empirical Bayes techniques, Fairness in Machine Learning, Statistical Machine Learning, and High Dimensional Statistics. Prior to joining the University of Sydney, Brad completed his Ph.D. in Statistics at the University of Southern California’s Marshall School of Business in the Department of Data Sciences and Operations, advised by Dr. Gareth James and Dr. Xin Tong. His graduate studies were supported by the NSF’s Graduate Research Fellowship, USC’s Marshall Fellowship, and USC’s Global Branding Fellowship. Brad also completed his undergraduate degree from USC in Applied Mathematics.

Bradley Rava is a researcher at the University of Sydney Business School with expertise in statistics, fairness, machine learning, and empirical bayes. His work focuses on developing fair and robust machine learning algorithms, particularly in classification tasks, while addressing issues such as bias correction and error control under imperfect supervision. Rava's research explores methods to enhance fairness in predictive models and improve probability estimates through theoretical and empirical approaches. His publications include studies on fairness-adjusted classification, label-noise-adjusted algorithms, and the correction of bias in probability estimates, contributing to the fields of statistical learning and algorithmic fairness.