Wesley Tansey, PhD, is an Associate 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.
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.
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
Loper, J.H., Lei, L., Fithian, W., and Tansey, W. (2022). Smoothed nested testing on directed acyclic graphs. Biometrika 109, 457–471. https://doi.org/10.1093/biomet/asab041.
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.
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
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.
Tansey W, Wang Y, Rabadan R, Blei DM. Double Empirical Bayes Testing. Int Stat Rev. 2020 Dec;88(Suppl 1):S91-S113. doi: 10.1111/insr.12430. PMID: 35356801
Sudarshan M, Tansey W, Ranganath R. Deep direct likelihood knockoffs. Neural Information Processing Systems (NeurIPS) (To Appear) (2020).
Burns C, Thomason J, Tansey W. Interpreting Black Box Models via Hypothesis Testing. ACM-IMS Foundations of Data Science. (2020).
Tansey W, Tosh C, Blei D. Relational Dose-Response Modeling for Cancer Drug Studies. arXiv:1906.04072v2 (2019).
Tansey W, Li K, …, et al. Dose-response modeling in high-throughput cancer drug screenings: An end-to-end approach. Biostatistics (To Appear) (2020).
Tansey W, Veitch V, Zhang H, Rabadan R, and Blei DM. The Holdout Randomization Test: Principled and Easy Black Box Feature Selection. arXiv:1811.00645, Preprint (November 2018).
Tansey W, Wang Y, …, et al. Black Box FDR. The 2018 International Conference on Machine Learning (ICML) (July 2018).
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