Featured Projects

Distributionally Robust Learning under Covariate Shift

This project covers a series of my work, ranging from fundamentals of distributionally robust learning under covariate shift, to its integration to real-world safe exploration and domain adaption tasks. Media Coverage: The Value of Saying ‘I Don’t Know’.

UQ for AI Safety and Fairness

We aim to tackle two key challenges in model auditing for safeguarding AI. The first is the ubiquitous distribution shift, especially subpopulation shift. The second is that many UQ approaches require either intensive computing power or an impractical amount or quality of data that may be unavailable in real-world scenarios. Media Coverage: Putting trust to the test.

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