I am a first year PhD student at Princeton Language and Intelligence (PLI) advised by Danqi Chen. My research interests broadly lie in the intersection of natural language processing and machine learning. I am currently interested in language models and agents; in particular, I aim to study the downstream effects of pretraining data and methods to improve the capabilities and efficiency of reasoning models.

Below are a few 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?
• How do we best correct misalignments arising from knowledge conflicts in models' pretraining data?

• 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 much better would reasoning models be if trained with process rewards rather than just outcome rewards?
• How can we construct environments with verifiable rewards and/or induce structure into reasoning chains to make models more capable and efficient?

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.