Epidemiology & Biostatistics
The Wesley Tansey Lab
Research
The Tansey lab focuses on solving frontier problems in cancer data science through the development of innovative statistical machine learning methods. How do we discover effective combination therapies when the search space of possible combinations is vast? Are there patterns of spatial architecture in the tumor microenvironment that predict whether a patient will respond to a specific therapy? How do we build powerful-yet-interpretable multimodal models of medical images, laboratory tests, and clinical records that can inform and improve treatment decisions in the clinic? The goal of our lab is to distill these kinds of important scientific questions into precise mathematical statements, then derive answers in the form of computationally efficient and statistically principled methods. We are interested in a number of areas in statistics and computer science, including graphical models, Bayesian methods, deep learning, hypothesis testing, conditional density estimation, spatial statistics, active learning, and causal inference. Ultimately, we seek to lay the statistical and computational foundations necessary to deliver on the promise of precision medicine: delivering the right treatment, for the right patient, at the right moment, and at the right dose.
People
Wesley Tansey, PhD
- The Tansey lab focuses on solving frontier problems in cancer data science through the development of innovative statistical machine learning methods.
- PhD in Computer Science, University of Texas at Austin
- TanseyW@mskcc.org
- Email Address
- 646-608-7669
- Office Phone
Members
PhD student in Statistics at Columbia University
PhD student in Statistics at Duke University
PhD student in Physiology, Biophysics, and Systems Biology at Weill Cornell Medical College
PhD student in Statistics and Data Sciences at the University of Texas at Austin
PhD student in Statistics at University of Michigan
MSK MIND Scholar
Associate Research Scientist
PhD student in the Tri-Institutional Program for Computational Biology and Medicine
PhD student in Computer Science at the University of Texas at Austin