Tianxi Li

Tianxi Li is an Assistant Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. Their research integrates statistical methodology with applications in network science, data privacy, and biomedical data analysis.

Their research interests lie at the intersection of statistics and network science, focusing on statistical modeling of complex networks, data privacy, network security, and biomedical applications such as neuroimaging and genomics. They develop adaptive and scalable methods for network estimation, community detection, and differential correlation analysis.

The recent publications demonstrate a consistent focus on advancing statistical tools for network-structured data, with increasing applications in neuroscience and cancer genomics. The work spans theoretical development (e.g., network growth models) and practical applications (e.g., glioblastoma gene modules), reflecting a balance between methodology and real-world impact.

Tianxi Li leads an active research program funded by the National Science Foundation, indicating recognition and support for their innovative work.

• Principal Investigator, Statistical tools for network security protection: from data privacy to threat detection, NSF (2024–2025)

They advise graduate students in statistics and data science, though specific advisees are not listed. Their collaborative network includes researchers in biostatistics, computer science, and machine learning, as evidenced by co-authorships and interdisciplinary projects.

Li's work contributes to the UN Sustainable Development Goals, particularly through advancements in data-driven solutions for secure and ethical data analysis.