Charles F. Van Loan, Cornell University, is the 2018 recipient of The John von Neumann Lecture prize in recognition of his pioneering contributions to research in numerical linear algebra and to the exposition of the subject. SIAM’s highest honor and flagship lecture, The John von Neumann Lecture, is awarded annually to recognize outstanding and distinguished contributions to the field of applied mathematical sciences and the effective communication of these ideas to the community. Van Loan will deliver The John von Neumann Lecture, “Untangling Random Polygons and Other Things,” at the SIAM Annual Meeting in Portland, Oregon on July 10, 2018.
From generalized singular value problems and structured eigenvalue problems, to matrix functions and most recently in tensor computations, Van Loan has led the way in developing theory and algorithms – with a particular view to applications in signal processing and control theory. SIAM recognizes him as a brilliant communicator, in any medium, for any audience. His book Matrix Computations, now in its fourth edition, is the canonical reference and has shaped the field and influenced the way we think about matrix computations.
Charles F. Van Loan is currently Professor Emeritus of Computer Science and Dean of the Faculty at Cornell University. He earned his Ph.D. in 1973 from the University of Michigan under the supervision of Cleve Moler. Since joining Cornell University in 1975, he has worked in the numerical linear algebra area with a focus on eigenvalue, least square, and linear system problems. Kronecker products also figure in much of his research.
Van Loan is the author of several textbooks including Matrix Computations, 4th Edition, with Gene H. Golub; Handbook for Matrix Computations with Thomas F. Coleman; Computational Frameworks for the Fast Fourier Transform; Introduction to Scientific Computing: A Matrix-Vector Approach Using MATLAB; and Insight Through Computing: A MATLAB Introduction to Computational Science and Engineering with K.-Y. Daisy Fan. Van Loan has been a member of SIAM for over 40 years and a Fellow of SIAM since 2009.
Q: Why are you excited to be winning the award of The John von Neumann Lecture?
A: Teaching and research are indistinguishable, and The John von Neumann Lecture gives me the chance to make this point using a matrix computation example. The example is interesting because it started out in a “Computer Science 1” classroom and ended up in SIAM Review.
Q: Could you tell us a bit about the research that won you the prize?
A: Just about all of my work over the last 50 years has been a stone’s throw away from the singular value decomposition (SVD) and related matrix factorizations. The generalized SVD, various “nearness” problems, total least squares, and tensor decompositions speak to that. The SVD provides remarkable insight across the breadth of computational mathematics, even with matrices as small as 2-by-2, as I show in my lecture.
Q: What does your research mean to the public?
A: Matrix computations is the “inner loop” of scientific computation, the bedrock upon which many advanced simulations and complicated optimizations are built. These simulations and optimizations in turn drive science and engineering, producing a great “Manhattan Skyline” of achievement that is appreciated by the public.
Q: What does being a SIAM member mean to you?
A: My professional life has been especially shaped by the SIAM Book Program and the SIAM Journals. And I have certainly benefited from SIAM’s success in growing the stature of applied and computational mathematics in all STEM fields.