Tian Bai
Undergraduate student at McGill University
I recently graduated from McGill University with an Honours B.Sc. in Mathematics and Computer Science and plan to pursue a Ph.D. beginning in 2025. My research interests focus on the intersection of statistics and machine learning, particularly in areas that include:
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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.
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Understanding foundational aspects of AI/ML Systems: I am fascinated by the inner workings of complex ML models, which are often opaque. My aim is to gain a deeper understanding of these systems or potentially enhance their transparency through structural modifications.
Currently, I am working on generalizations and applications of conformal inference under the supervision of Prof. Archer Y. Yang.
You can download my CV here.
news
| Nov 28, 2024 | Vanilla conformal selection requires a predefined conformity score function, which can limit selection power by preventing score optimization without additional data splitting. Checkout OptCS, a general framework that enables statistical testing (selection) after flexible data-driven model optimization! |
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| Nov 01, 2024 | My paper comparing conformal selection with traditional methods in drug pre-screening is now available as a preprint! |
| Sep 28, 2024 | I’m pleased to announce that our paper on using machine learning to predict patient outcomes in emergency department triage has been accepted for publication in the Canadian Journal of Emergency Medicine! |
| May 15, 2024 | My personal website is now live. |