Zhongxuan Sun

About

I am a PhD student in Biomedical Data Science at the University of Wisconsin–Madison, co-advised by Prof. Sündüz Keleş and Prof. Hyunseung Kang. My research develops methods for causal inference and statistical genomics, motivated by challenges that arise in the analysis of complex biomedical data.

My current work centers on large-scale CRISPR perturbation data, with applications to studying gene regulation and other complex biological processes. Before graduate school, I worked with Prof. Qiongshi Lu on statistical methods for complex trait genetics and genetic risk prediction using biobank-scale data.

Background

Selected Publications

* Equal contribution.

First / Co-first author

• Sun, Z., Kang, H., Keleş, S. (2026). Causal gene regulatory network inference from Perturb-seq via adaptive instrumental variable modeling. Research in Computational Molecular Biology (RECOMB). (Acceptance Rate: 15.8%).
• Wu, Y.*, Sun, Z.*, Zheng, Q., Miao, J., Dorn, S., Mukherjee, S., Fletcher, J., Lu, Q. (2024). Pervasive biases in proxy genome-wide association studies based on parental history of Alzheimer’s disease. Nature Genetics.

Co-author

• Park, K., Sun, Z., Liao, R., Bresnick, E. H., Keleş, S. (2026). Systematic background selection with BasCoD enhances contrastive dimension reduction in single cell genomics. Nature Communications.
• Zhao, Z., Gruenloh, T., Yan, M., Wu, Y., Sun, Z., Miao, J., Wu, Y., Song, J., Lu, Q. (2024). Optimizing and benchmarking polygenic risk scores with GWAS summary statistics. Genome Biology.
• Miao, J., Wu, Y., Sun, Z., Miao, X., Lu, T., Zhao, J., Lu, Q. (2024). Valid inference for machine learning-assisted genome-wide association studies. Nature Genetics.

Full list on Google Scholar.

Presentations & Recognition

Service