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Hi there! I'm Tiffany.

I am a second-year PhD student in the UC Berkeley Statistics Department advised by Michael I. Jordan. My work is supported by the NSF Graduate Research Fellowship.

I am broadly interested in developing methods and theory to address problems that emerge when machine learning models are applied to the real world. Some of the questions I work on are: Can we quantify the uncertainty of black-box machine learning models so that we don't have to blindly trust their output? How can we train a model when the underlying data distribution is changing over time?

Previously, I received an ScB in Applied Mathematics and an ScM in Computer Science from Brown University, where I was fortunate to work with Elizabeth Chen, Chip Lawrence, and Stephen Bach. I have also spent summers at Facebook and the Johns Hopkins University Applied Physics Laboratory.

Feel free to connect with me via email at tiffany_ding[at]berkeley[dot]edu or on LinkedIn.