Tian Bai

I am a first-year Statistics Ph.D. student at Stanford University. I am broadly interested in statistics and statistical machine learning, and I am particularly piqued by areas such as:

• Enhancing reliability and trustworthiness of AI/ML Systems: I am interested in the development and application of methods for accessing and improving the reliability of black-box AI/ML systems (e.g. uncertainty quantification, statistical inference with AI systems), enabling confident deployments in risk-sensitive settings.

• Understanding foundational aspects of AI/ML Systems: I am fascinated by the inner workings of complex ML models, which are often opaque. I am interested in developing a deeper understanding of these systems and exploring ways to improve their transparency through principled structural or methodological modifications.

Most recently, my work has centered on generalizations and applications of conformal inference.

Before starting my Ph.D., I graduated from McGill University, where I studied mathematics, computer science, and statistics. During my undergraduate studies, I had the privilege of working with Prof. Archer Y. Yang and Prof. Ying Jin.

You can download my CV here.