Vanderbilt University
Institute of Imaging Science
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Simon Vandekar

Assistant Professor of Biostatistics

Contact Information

simon.vandekar@vumc.org
(615) 322-2001

Interests

My interests pertain to statistical issues in medical image analysis such as quantification of uncertainty and replicability and performing high dimensional inference.

Projects

My work lately has focused on making accurate probabilistic statements for high-dimensional neuroimaging data such as computing voxel-wise and spatial extent adjusted p-values that describe how unlikely the observed results are under a particular null hypothesis. In imaging this requires dealing with low-rank estimates for complex covariance matrices, issues of normal approximations in high-dimensional data, and model misspecification. I developed several new (semi)parametric bootstrap joint (PBJ) inference procedures, which are available as an R package (pbj) on github. My current research is attempts to accurately estimate the probability that a thresholded statistical image contains all truly activated regions using small sample sizes. This is more akin to constructing confidence intervals than hypothesis testing and affords more power when the null hypothesis is false. I am also interested in the estimation of the proportion of the image with a nonzero effect size, inference for machine learning models of imaging data, topological data analysis, and unsupervised deep learning.

Publications

Alexander-Bloch, A.F., Shou, H., Liu, S., Satterthwaite, T.D., Glahn, D.C., Shinohara, R.T., Vandekar, S.N., Raznahan, A., 2018. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage 178, 540?551. doi: 0.1016/j.neuroimage.2018.05.070

Vandekar, S.N., Reiss, P.T., Shinohara, R.T., 2018a. Interpretable High-Dimensional Inference Via Score Projection With an Application in Neuroimaging. Journal of the American Statistical Association 0, 1?11. doi: 10.1080/01621459.2018.1448826

Vandekar, S.N., Satterthwaite, T.D., Rosen, A., Ciric, R., Roalf, D.R., Ruparel, K., Gur, R.C., Gur, R.E., Shinohara, R.T., 2017. Faster family-wise error control for neuroimaging with a parametric bootstrap. Biostatistics. doi: 10.1093/biostatistics/kxx051

Vandekar, S.N., Satterthwaite, T.D., Xia, C.H., Ruparel, K., Gur, R.C., Gur, R.E., Shinohara, R.T., 2018b. Robust Spatial Extent Inference with a Semiparametric Bootstrap Joint Testing Procedure. arXiv:1808.07449 [stat].

Vandekar, S.N., Shinohara, R.T., Raznahan, A., Roalf, D.R., Ross, M., DeLeo, N., Ruparel, K., Verma, R., Wolf, D.H., Gur, R.C., Gur, R.E., Satterthwaite, T.D., 2015. Topologically Dissociable Patterns of Development of the Human Cerebral Cortex. J. Neurosci. 35, 599?609. 10.1523/JNEUROSCI.3628-14.2015