Hello! I am a fourth-year Ph.D. student in the Department of Computer Science at Purdue University, advised by Dan Goldwasser. My work centers on using natural language processing to understand and draw conclusions from large amounts of unstructured data. In particular, I've built scalable and interpretable NLP systems that reason about human behavior, beliefs, emotions, and values as expressed in noisy real-world corpora—especially social media.

My research is driven by the concept of abstraction: understanding how a single underlying idea manifests across multiple data points. By effectively modeling these abstractions, we gain the ability to interact with and manipulate complex information more efficiently and intuitively.

From a technical standpoint, my work extensively involves large-scale text and video processing, retrieval, reranking, text clustering, and full-pipeline dataset design, collection, and validation. I build these systems using real, messy data sources across multiple modalities, and have applied this technical foundation in resource-intensive industry settings, including my time as an Applied Science intern at AWS AI.

Before coming to Purdue, I earned my B.Sc. in Computer Science and Mathematics from the University of Nebraska-Lincoln, where I was first introduced to ML in working with Stephen Scott and M. R. Hasan.