New algorithms for diagnosing line failures in a smart grid.
Fast spectral tools for graph structure.
Generalizing classic eigenvalue localization results (e.g. Gershgorin).
Asynchronous parallel algorithms for finding minima fast by fitting functions to surrogate models.
CU Scientific Software Club
Numerical Methods for Data Science (SJTU CS 259)
Short course offered June-July 2018 at SJTU.
Survey course of numerical methods prominent in modern data analysis and machine learning. Building on basic methods of optimization and numerical linear algebra, the course will explore the role of numerical methods for treating several classes of data analysis problems, including low rank factorizations and completion of matrix data; function approximation by kernel methods; and analysis of data on graphs.