People / Students / Class of 2025
Runxuan (Renee) Li is a highly aspiring and versatile data scientist, recently graduating from the University of Wisconsin-Madison with dual degrees in Data Science and Statistics, achieving an impressive GPA of 3.935. Her academic excellence has been recognized through multiple honors, including the Dean’s List for multiple semesters, a Study Abroad Scholarship, and a Summer Term Scholarship. Her robust academic foundation is complemented by a diverse technical skill sets that span both data science and biology-related techniques. Runxuan is proficient in Python, R, Stata, MySQL, and Hadoop, as well as bioinformatics tools such as PCR, molecular cloning, and DNA/protein isolation. This combination of skills enables her to approach data science problems from multiple angles, making her well-equipped to handle complex and interdisciplinary projects. Her research experience demonstrates her ability to apply data science methodologies to real-world scientific challenges. In the Mup Evolution Project at Dewey Lab, she processed and analyzed over 12GB of genomic data, including millions of base pairs from Ensembl. Using Python, she automated the extraction of biological insights from GTF files, which significantly reduced manual work and increased analysis speed. Her analysis of whole genome alignments involved the mapping of over 3582 genes, where she uncovered genetic variations and mutations crucial to understanding evolutionary patterns. Her work not only contributed to a deeper understanding of the evolutionary dynamics of the Mup gene but also showcased her ability to handle large-scale data, perform complex bioinformatics analyses, and interpret genetic patterns. In her role at the Ragsdale Lab, Runxuan worked with genetic data files containing 197 sequences totaling 23 million base pairs to understand evolutionary trends. Her research on diversity patterns of drosophila genes led to a clearer understanding of selection pressures, significantly contributing to evolutionary biology research. Additionally, during the Field Day VR project, she processed tens of thousands of data points from user interactions, developed custom algorithms to track time-based metrics, and extracted valuable user behavior insights, helping the team refine the VR game experience. During Runxuan’s internship at Enspec, she worked on processing hyperspectral imagery and optimizing data workflows using Python-based object-oriented frameworks. She contributed to mission planning and data processing tasks, displaying a strong command of both technical and practical aspects of data management. Her role as a data analyst intern at GozenData involved conducting in-depth analysis of TV audience behaviors, leading project management efforts, and evaluating cloud infrastructure solutions. These experiences highlight her ability to apply her data science skills in diverse professional settings, from bioinformatics research to business analytics and project management. Overall, Runxuan’s ability to process large-scale datasets, apply advanced data science techniques, and deliver actionable insights has made her an impactful data scientist across both research and professional settings.
