Wesley Tansey, PhD

Assistant Attending, Computational Oncologist

Wesley Tansey, PhD

Assistant Attending, Computational Oncologist
Wesley Tansey

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The University of Texas at Austin

Wesley Tansey, PhD, is an Assistant Attending in the Computational Oncology Service. Dr. Tansey’s research is at the intersection of statistics and computing, with a focus on principled machine learning methods motivated by problems in cancer biology and medicine. His lab develops new methods to address the data science challenges raised by emerging technologies, such as high-throughput screening and single-cell sequencing. Dr. Tansey has published articles in premier statistics journals and machine learning conferences on a wide range of methodological areas, ranging from graphical models and Bayesian statistics to deep learning. Dr. Tansey has a PhD in Computer Science from the University of Texas at Austin and has trained at Columbia University and Columbia University Medical Center. He is a co-organizer of the Workshop on Computational Biology at the International Conference on Machine Learning and a member of the editorial board of the Journal of Machine Learning Research.


  1. Zhang H, Hunter MV, Chou J, Quinn JF, Zhou M, White RM, Tansey W. BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment. Cell Syst. 2023 Jul 19;14(7):605-619.e7. doi: 10.1016/j.cels.2023.06.003. PMID: 37473731; PMCID: PMC10368078.
  2. Freeman BA, Jaro S, Park T, Keene S, Tansey W, Reznik E. MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization. Genome Biol. 2022 Sep 1;23(1):184. PMCID: PMC9438248
  3. Tansey W, Tosh C, Blei DM. A Bayesian model of dose-response for cancer drug studies. aoas. Institute of Mathematical Statistics; 2022 Jun;16(2):680–705.
  4. Tansey W, Li K, Zhang H, Linderman SW, Rabadan R, Blei DM, Wiggins CH. Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach. Biostatistics. 2022 Apr 13;23(2):643-665. doi: 10.1093/biostatistics/kxaa047. PMID: 33417699
  5. Tansey W, Veitch V, Zhang H, Rabadan R, Blei DM. The Holdout Randomization Test for Feature Selection in Black Box Models. J Comput Graph Stat. Taylor & Francis; 2022 Jan 2;31(1):151–162.

View a full listing of Wesley Tansey’s journal articles.


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Wesley Tansey discloses the following relationships and financial interests:

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