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| Van Loan, Charles | |
|---|---|
| Name | Charles Van Loan |
| Birth date | 1950s |
| Birth place | Cambridge, Massachusetts |
| Nationality | American |
| Fields | Numerical analysis, Computer science, Applied mathematics |
| Workplaces | Cornell University |
| Alma mater | Massachusetts Institute of Technology, University of Michigan |
| Doctoral advisor | Gene H. Golub |
| Known for | Numerical linear algebra, matrix computations, algorithms |
Van Loan, Charles
Charles Van Loan is an American researcher in numerical analysis and computer science noted for contributions to numerical linear algebra, matrix algorithms, and scientific computing. He has held a long academic appointment at Cornell University and collaborated with scholars across institutions such as the Massachusetts Institute of Technology and the University of Michigan. His work bridges theory and implementation, influencing software projects, graduate education, and engineering practice.
Van Loan was born in Cambridge, Massachusetts and raised in a milieu shaped by nearby institutions including Harvard University and Massachusetts Institute of Technology. He earned his undergraduate degree at the Massachusetts Institute of Technology, where he studied topics that connected to applied mathematics and computer science. For graduate study he attended the University of Michigan, completing a Ph.D. under the supervision of Gene H. Golub, a prominent figure associated with Stanford University and the development of matrix computation software such as LINPACK and EISPACK. His doctoral training placed him within a lineage that includes contributors to the National Science Foundation-supported numerical libraries and to applied projects involving Argonne National Laboratory.
Van Loan joined the faculty of Cornell University, where he rose through the ranks to become a senior professor in departments connected to Computer Science and Applied Mathematics. At Cornell University he taught courses that interfaced with curricula at the School of Electrical and Computer Engineering and graduate programs linked to the Institute for Computational Science. He has supervised doctoral students who went on to positions at institutions including University of California, Berkeley, Princeton University, Massachusetts Institute of Technology, and industrial research groups such as Bell Labs and IBM Research. Van Loan has served on committees of professional organizations including SIAM and ACM, participated in program committees for conferences like the International Conference on Computational Science, and contributed to workshops at Los Alamos National Laboratory.
Van Loan's research centers on matrix computations, algorithms for eigenvalue problems, and numerical methods for large-scale systems. He has published work on topics such as QR algorithms, singular value decomposition, and structured matrix factorizations, relating to software systems like LAPACK and ScaLAPACK. His studies on Krylov subspace methods and Arnoldi/Lanczos-type procedures connect to implementations employed at Argonne National Laboratory and in high-performance computing projects at Oak Ridge National Laboratory. Van Loan explored stability analysis for floating-point computations, linking to standards and practices developed by groups such as IEEE 754 committees and influencing benchmarking efforts like those associated with the Top500 list. Collaborations with researchers from Stanford University, UC San Diego, University of Oxford, and ETH Zurich broadened applications to control theory problems encountered at NASA and in signal processing applications linked to Bell Labs and AT&T research divisions.
He also contributed to pedagogy and software, translating theoretical insights into code and educational material that interfaced with projects such as MATLAB toolboxes and course offerings that paralleled initiatives at Carnegie Mellon University and University of Illinois Urbana-Champaign. Van Loan’s work on matrix exponentials and matrix functions informed computational practice in areas including systems biology models developed at Broad Institute and computational finance models used in industry hubs like Wall Street research groups.
Van Loan authored and coauthored influential texts and articles that have been adopted in graduate curricula at institutions including Harvard University, Princeton University, and Stanford University. His textbooks cover numerical linear algebra, matrix computations, and algorithmic techniques; these works are cited alongside classics by Gene H. Golub and James Demmel. He contributed chapters to edited volumes published by organizations such as SIAM and Springer, and published papers in journals including the SIAM Journal on Matrix Analysis and Applications and the ACM Transactions on Mathematical Software. Course notes and lecture series by Van Loan have been used in summer schools sponsored by NSF and by research centers like Mathematical Sciences Research Institute.
Throughout his career Van Loan received recognition from academic and professional bodies. He has been awarded fellowships and honors associated with SIAM, and held visiting appointments at institutions such as Courant Institute of Mathematical Sciences and University of Cambridge. His contributions to numerical software and education were acknowledged by awards from university-level teaching committees at Cornell University and by invitations to deliver named lectures at venues including Stanford University and Imperial College London. Panels and committees of the National Science Foundation and editorial boards of journals like Numerische Mathematik have also recognized his expertise.
Van Loan’s personal life has been intertwined with academic communities in the Ithaca, New York area and with broader networks linking Boston and technical centers on the U.S. East Coast. He has mentored generations of students who occupy faculty positions at University of Washington, University of Texas at Austin, Duke University, and in industry at Google Research and Microsoft Research. His legacy includes influence on numerical libraries such as LAPACK, textbooks used across Columbia University and Yale University courses, and a lineage of researchers active in conferences like the International Congress on Industrial and Applied Mathematics and in societies including ACM and SIAM.
Category:American mathematicians Category:Numerical analysts Category:Cornell University faculty