The Arkansas Spring Lecture Series in the Mathematical Sciences are research conferences focused on a specific topic chosen among the current leading research areas.
A principal lecturer is invited to deliver a five-lectures course, and ten specialists give talks on subjects closely related to the conference main topic. Short talks by young PhDs and finishing graduate students are solicited to complement the conference.
Since its inception in 1977, the Arkansas Spring Lecture Series has grown into an ideal opportunity for specialists and young researchers to meet and exchange ideas about topics at the forefront of modern mathematics and statistical sciences. In recent years, a number of outreach activities, in the form of an evening public lecture and dedicated discussion panels, have been added to benefit the local learning community.
Tulin Kaman of the Department of Mathematical Sciences at the University of Arkansas, is the organizer of the 47th Annual Spring Lecture Series in Numerical Linear Algebra.
These annual conferences focus on topics that are at the forefront of current mathematical research. The SLS provides a unique venue to facilitate interactions between junior researchers and their more senior colleagues. Over the past forty years the conference format has been tested and adapted to optimize this goal. Thanks to the continued NSF support through the years, this series of conferences has grown into a highly effective vehicle for the dissemination of groundbreaking new research in the mathematical sciences and a forum for the discussion of new open problems.
Please access the slides with the download button and the video within the information (metadata).
Lectures from 2021
Lecture 00: Opening Remarks: 46th Spring Lecture Series, Tulin Kaman
Lecture 01: Scalable Solvers: Universals and Innovations, David Keyes
Lecture 02: Tile Low-rank Methods and Applications (w/review), David Keyes
Lecture 03: Hierarchically Low Rank Methods and Applications, David Keyes
Lecture 04: Spatial Statistics Applications of HRL, TRL, and Mixed Precision, David Keyes
Lecture 05: The Convergence of Big Data and Extreme Computing, David Keyes
Lecture 06: The Impact of Computer Architectures on the Design of Algebraic Multigrid Methods, Ulrike Yang
Lecture 07: Nonlinear Preconditioning Methods and Applications, Xiao-Chuan Cai
Lecture 08: Partial Eigen Decomposition of Large Symmetric Matrices via Thick-Restart Lanczos with Explicit External Deflation and its Communication-Avoiding Variant, Zhaojun Bai
Lecture 09: Hierarchically Low Rank and Kronecker Methods, Rio Yokota
Lecture 10: Preconditioned Iterative Methods for Linear Systems, Edmond Chow
Lecture 11: The Road to Exascale and Legacy Software for Dense Linear Algebra, Jack Dongarra
Lecture 12: Recent Advances in Time Integration Methods and How They Can Enable Exascale Simulations, Carol S. Woodward
Lecture 13: A low-rank factorization framework for building scalable algebraic solvers and preconditioners, X. Sherry Li
Lecture 14: Randomized Algorithms for Least Squares Problems, Ilse C.F. Ipsen
Public Lecture: Harnessing the Power of Mathematics for High Performance Computing, Ann Almgren