Bradley Rava

Bradley Rava is a Lecturer in Business Analytics at the University of Sydney's Business School. His research focuses on modern statistical methods addressing societal challenges in automated decision-making, particularly in high-risk domains like healthcare and finance. He holds a PhD in Statistics from the University of Southern California, advised by Dr. Gareth James and Dr. Xin Tong, supported by prestigious fellowships including the NSF Graduate Research Fellowship and USC Marshall Fellowship.

Education:

• Ph.D. in Statistics, University of Southern California (2017–2022)
• B.S. in Applied and Computational Mathematics, University of Southern California (2013–2016)
• Emerging Scholars Fellowship, Yale University (2016–2017)

Research Interests: Empirical Bayes techniques, fairness in machine learning, statistical machine learning, high-dimensional statistics, and uncertainty communication in automated systems. His work emphasizes rigorous control of severe consequences in AI-driven decisions.

Awards & Honors:

• NSF Graduate Research Fellowship
• USC Marshall Fellowship
• Correlation-One Southern California Datathon 1st place
• Sydney Business School Early Career Research Grant

Teaching & Grants: Teaches Advanced Applications in Business Analytics and Statistical Learning. Received grants for uncertainty estimation in fair classification and interdisciplinary collaborations with Cornell.

Professional Activities: Active in conferences (INFORMS, JSM), invited speaker on fairness in AI, and founder of the LSESU Applicable Maths Society.