Wesley Tansey, PhD

Assistant Attending
Epidemiology & Biostatistics
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Wesley Tansey, PhD

Assistant Attending
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Wesley Tansey

Office Phone

646-608-7669

Email

[email protected]

URL

View Wesley Tansey's Lab

Education

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.

Publications

  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.

Disclosures

Doctors and faculty members often work with pharmaceutical, device, biotechnology, and life sciences companies, and other organizations outside of MSK, to find safe and effective cancer treatments, to improve patient care, and to educate the health care community.

MSK requires doctors and faculty members to report (“disclose”) the relationships and financial interests they have with external entities. As a commitment to transparency with our community, we make that information available to the public.

Wesley Tansey discloses the following relationships and financial interests:

No disclosures meeting criteria for time period


The information published here is for a specific annual disclosure period. There may be differences between information on this and other public sites as a result of different reporting periods and/or the various ways relationships and financial interests are categorized by organizations that publish such data.

This page and data include information for a specific MSK annual disclosure period (January 1, 2022 through disclosure submission in spring 2023). This data reflects interests that may or may not still exist. This data is updated annually.

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