Yuren actually obtained three undergraduate degrees at UW-Madison: Computer Sciences, Economics, and Mathematics. After UW, Yuren moved on to the Computer Science Department at Stanford for a Masters, to develop a machine learning pipeline that detects frog calls in order to help biologists understand frog mating patterns.

Yuren is interested in software engineering, database systems, and data processing. Previously, she interned at Sisu Data and worked on the staging reconciliation project to reduce data staging time and the query observability project involving collecting query execution metadata to help engineers understand the causes of slow queries. She interned at AWS to develop a non-data dependent issues to improve debugging abilities for Redshift.

On the research side, Yuren is broadly interested in database systems and diverse forms of data, with a particular emphasis on their applications in machine learning and interdisciplinary domains such as ecology, conservation, and biology. Her work seeks to address challenging problems by leveraging data-driven approaches. For example, she has applied acoustic data analysis to the study of marine ecosystems and explored the use of neural network models for the classification of animal species and Pseudomonas sequence data.