In recent years, we have seen a large body of research using hierarchical
matrix algebra to construct low complexity linear solvers and preconditioners.
Not only can these fast solvers significantly accelerate the speed of
large scale PDE based simulations, but also they can speed up many AI and
machine learning algorithms which are often matrix-computation-bound.
On the other hand, statistical and machine learning methods can be used
to help select best solvers or solvers' configurations for specific problems
and computer platforms. In both of these fields, high performance computing
becomes an indispensable cross-cutting tool for achieving real-time solution
for big data problems. In this talk, we will show our recent developments
in the intersection of these areas.

BIO
Sherry Li is a Senior Scientist in the Computational Research Division,
Lawrence Berkeley National Laboratory. She has worked on diverse problems
in high performance scientific computations, including parallel computing,
sparse matrix computations, high precision arithmetic, and combinatorial
scientific computing. She is the lead developer of SuperLU, a widely-used
sparse direct solver, and has contributed to the development of several other
mathematical libraries, including ARPREC, LAPACK, PDSLin, STRUMPACK, and XBLAS. She earned Ph.D. in Computer Science from UC Berkeley and B.S. in Computer Science from Tsinghua Univ. in China. She has served on the editorial boards of the SIAM J. Scientific Comput. and ACM Trans. Math. Software, as well as many program committees of the scientific conferences. She is a Fellow of SIAM and a Senior Member of ACM.