Use of Differential Geometry for Identification of Features in Spinal Images Predictive of Lower Back Pain, Stanford University and INRIA Sophia-Antipolis (1/1/2013)
Using modern Monte Carlo Markov chain techniques to generate posterior distributions on the space of transformations on manifolds, we are discovering the geometric features predictive of back pain using high resolution imagery.
Current Research Interests:
Our work focuses on large heterogeneous multi-layer data analyses. Whether using image analysis and segmentation for the study of cancer and immune cell interactions, or brain imaging and DNA sequence analyses for the study of dependencies between genetic and neurological dynamics, all these statistical studies have involved large complex datasets of different types where dynamics of interactions between different components of a system are the key to understanding the underlying biology.
We have generalized methods such as Principal Components Analysis (PCA) to more diverse data...