I am a second year PhD student at Princeton Language and Intelligence (PLI) advised by Danqi Chen. My research interests are grounded in questions that challenge the current paradigm of the language model lifecycle. I aim to identify surprising phenomena and shortcomings exhibited by current models and come up with approaches to remedy them. Below are examples of questions I am interested in:

• How does pretraining data influence language models as sources of knowledge? [Dated Data]
• Can we attribute content generated by models back to their pretraining corpus?
• Is it possible for models to continuously update their stale knowledge through continued pretraining without degrading performance?

• Can we make reasoning models more efficient by shifting away from a discrete token space and perform reasoning in continuous latent space? [ Compressed Chain of Thought ]
• How can we make models better at explaining their choices in domains that require state-tracking and search such as chess? [ Chess-Language Models ]
• Is it possible to calibrate language model responses to correspond to their internal uncertainty?

Previously, I recieved my Master's at Johns Hopkins University, advised by Benjamin Van Durme. Prior to NLP, my interests were in mathematics and fluid dynamics. I conducted research in these areas during my undergraduate studies at Duke University, advised by Tarek Elgindi.