I am a postdoctoral researcher at Allen Institute of Artificial Intelligence, working with Yejin Choi, as part of the MOSAIC team.
My research interests include studying bias in datasets - the good, the bad and the ugly. Good biases, such as structural inductive biases beneficial for language understanding, I wrote a PhD thesis on these. On the other hand, crowd-sourced, large-scale datasets are riddled with annotation artifacts which are spurious correlations with unintended effects; I call these the bad biases. And finally, biases can be ugly, when the training data contains a large portion of mislabeled examples, or noise (more on this soon!).
I obtained my PhD from Carnegie Mellon University in May 2019, where I was advised by Noah Smith and Chris Dyer. During most of my PhD I was a visiting student at the Paul G. Allen School of Computer Science at the University of Washington in Seattle.