Dr. David Huckleberry Gutman has received a NSF CAREER Award for his proposal titled, “Fast Algorithms for Riemannian Optimization.”
This award's overarching goal is to construct new methods for solving Riemannian optimization problems with the fastest possible computational speed. Tangible benefits of this award will include new software packages for easily solving Riemannian optimization problems, new educational materials that introduce this exciting field to undergraduate and graduate students, and funding for graduate students in a research group predominantly comprised of underrepresented minorities.
Riemannian optimization, the study of minimizing a cost function over a Riemannian manifold, is surging in prominence due to its many applications in modern statistics and machine learning. A small sample of these popular applications includes metric learning, mixture model parameter estimation, covariance estimation and subspace recovery, and matrix completion. In the non-statistical realm, Riemannian optimization is becoming an important toolset for diffusion tensor imaging, a novel technology for using magnetic resonance imaging to profile the human brain, as well as for solving synchronization of rotation problems that support 3-D imaging of real-world objects.
For more information on TTUIMSE visit:
https://www.depts.ttu.edu/imse/ or call 806.742.3543.
About Dr. David Huckleberry Gutman
Dr. David Huckleberry Gutman is an assistant professor in the Department of Industrial, Manufacturing, and Systems Engineering (IMSE) at Texas Tech University. His expertise is continuous optimization theory with an emphasis on methods for large-scale optimization and optimization over manifolds.