It features an anatomically guided sampling method to retrospectively eliminate problematic data and a parallel Monte Carlo software package to compute the Jacobian matrix for the inverse problem.
A recurring theme in his current research is the development of efficient techniques for matrix problems that involve Kronecker products and multiple symmeties.
The resulting algorithms are expected to achieve unparalleled results for scalable matrix optimization problems in signal processing, machine learning, and computer science.
Theory and algorithm development for large matrix computation problems arising in computational science and engineering.
Accurate estimation of the Fisher information matrix for the PET image reconstruction problem.
We have developed new communication-avoiding algorithms for problems with all-to-all interactions such as many-body and matrix computations, taking into account their sparsity patterns, either from cutoff distance, symmetry, or data sparsity.
This talk presents highly scalable parallel algorithms for problems with sparse all-to-all interactions such as many-body and matrix computations.
Continuation of topics introduced in 2.03, including work-energy concepts, Lagrange's equations for systems of particles and rigid bodies in planar motion, and matrix eigenvalue problems.
Global Convergence of Stochastic Gradient Descent for Some Nonconvex Matrix Problems Christopher De Sa, Kunle Olukotun, and C. Ré.
Least squares and matrix perturbation problems.
Wolters, C. H., Grasedyck, L. & Hackbusch, W. Efficient computation of lead field bases and influence matrix for the FEM-based EEG and MEG inverse problem.
