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Yang Lu

Yang Lu

Assistant Professor

Before joining UW-Madison, Yang Lu was an assistant professor at the School of Computer Science, University of Waterloo. Before that, he was a postdoctoral researcher in Dr. William Noble’s group at the University of Washington. He obtained his Ph.D. in Computational Biology and Bioinformatics under the supervision of Dr. Fengzhu Sun from the University of Southern California.

Before moving to the United States, he received MS and BS degrees in Computer Science and Engineering from Shanghai Jiao Tong University. Yang Lu’s research focuses on developing machine learning and statistical methods for genomics and proteomics data analysis. He is particularly interested in developing interpretation methods to find scientifically interesting and statistically confident hypotheses from complex biological data.

Department

Biomedical Engineering

Contact

Engineering Centers Building
1550 Engineering Dr
Madison, WI

Featured news

New tool interrogates machine learning models to uncover disease-leading biomarker interactions

October 8, 2025

Focus on new faculty: Yang Lu brings AI to biology

September 2, 2025
View more articles with Yang Lu

  • PhD 2017, University of Southern California, Los Angeles
  • MS 2013, Shanghai Jiao Tong University
  • BS 2010, Shanghai Jiao Tong University

  • Data integration
  • Data-driven hypothesis generation
  • Multi-omics analysis
  • Agent-based user interaction

  • 2021 Machine-learning Methods for Single-cell Analysis Workshop, ACM-BCB, Most Innovative Presentation Award
  • 2013 University of Southern California, Los Angeles, Provost’s Fellowship
  • 2010 Shanghai Jiao Tong University, Shanghai, Graduate Fellowship
  • 2008 Shanghai Jiao Tong University, Shanghai, National Endeavor Scholarship

  • Chen, W., Jiang, Y., Noble, W. S., & Lu, Y. (2025). Error-controlled non-additive interaction discovery in machine learning models. Nature Machine Intelligence (In press).
  • Kertesz-Farkas, A., Acquaye, Frank Lawrence Nii Adoquaye,, Ostapenko, V., Locon, R. H., Lu, Y., Grant, C. E., & Noble, W. S. (2025). Fast and Memory-Efficient Searching of Large-Scale Mass Spectrometry Data Using Tide. Journal of Proteome Research.
  • Jiang, Y., Atton, M., Zhu, Q., & Lu, Y. (2025). MIOSTONE: Modeling microbiome-trait associations with taxonomy-adaptive neural networks. Microbiome, 13(1), 87.
  • Schreiber, J., Lorbeer, F. K., Heinzl, M., Lu, Y., Stark, A., & Noble, W. S. (2025). Programmatic design and editing of cis-regulatory elements. bioRxiv, 2025-04.
  • Zhou, H., Cao, K., & Lu, Y. (2025). Securing diagonal integration of multimodal single-cell data against ambiguous mapping. Bioinformatics, 41(6), btaf345.
  • Chen, W., Noble, W. S., & Lu, Y. (2024). DeepROCK: Error-controlled interaction detection in deep neural networks. In NeurIPS Intepretable AI Workshop.
  • Lu, Y., Noble, W. S., & Keich, U. (2024). A BLAST from the past: revisiting blastp's E-value. Bioinformatics, 40(12), btae729.
  • Alipour, F., Holmes, C., Lu, Y., Hill, K. A., & Kari, L. (2024). Leveraging machine learning for taxonomic classification of emerging astroviruses. Frontiers in Molecular Biosciences, 10, 1305506.
  • Jiang, Y., Liao, D., Zhu, Q., & Lu, Y. (2024). PhyloMix: Enhancing microbiome-trait association prediction through phylogeny-mixing augmentation. Bioinformatics, btaf014.
  • Dong, Z., Zhong, V., & Lu, Y. (2023). BioMANIA: Simplifying bioinformatics data analysis through conversation. bioRxiv, 2023-10.