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Events

Andrea Bertozzi - UCLA
Mar 30, 3:15pm
Applied Mathematics - Lecture - McCloska Auditorium, MTCC
Abstract: There is an extensive applied mathematics literature developed for problems in the biological and physical sciences. Our understanding of social science problems from a mathematical standpoint is less developed, but also presents some very interesting problems, especially for young... read more
Mon.
Mar 30
Professor Shuwang Li
Apr 1, 4:40pm
Applied Mathematics - Seminar - E1 025
Wed.
Apr 1
Matthew Dixon - University of San Francisco
Apr 2, 2:00pm to 3:00pm
Applied Mathematics - Lecture - Stuart Building - Room 220
Thu.
Apr 2
Songting Luo - Iowa State University
Apr 6, 4:40pm
Applied Mathematics - Colloquia - LS 152
Abstract: We present a Eulerian geometrical-optics method, namely fast Huygens sweeping method, for numerical solutions of Helmholtz equations with point sources. The method combines the geometrical optics approximations and Huygens secondary sources principle in a way that geometrical optics... read more
Mon.
Apr 6
Hualong Feng
Apr 8, 4:40pm
Applied Mathematics - Seminar - E1 025
Wed.
Apr 8
Cyril Imbert - Université Paris-Est Créteil
Apr 13, 4:40pm
Applied Mathematics - Colloquia - LS 152
In this talk, Imbert will present results obtained with P. Biler and G. Karch about a porous medium equation whose pressure law is non-local and non-linear. The results concern the existence of signed weak solutions, explicit self-similar compactly supported solutions and finite speed of... read more
Mon.
Apr 13
Apr 15, 4:40pm
Applied Mathematics - Colloquia - LS 152
Abstract: The Discrete Fourier Transform (DFT) is one of the most useful and powerful transformations in science and engineering. It is also a wonderfully efficient algorithm. Interestingly, when the data is sparse we can do better. We discuss algorithms for the sparse Fourier transform problem, in... read more
Wed.
Apr 15
Steven Damelin - Mathematical Reviews, American Mathematical Society
Apr 20, 11:25am
Applied Mathematics - Seminar - E1 102
Abstract: Kernel machines such as the Support Vector Machine are attractive because they can approximate functions or decision boundaries arbitrarily well with enough training data. Unfortunately, methods that operate on the kernel matrix (Gram matrix) of massive data sets in high dimensions... read more
Mon.
Apr 20
Matthew Lorig - University of Washington
Apr 20, 4:40pm
Applied Mathematics - Colloquia - LS 152
Mon.
Apr 20
Julienne Kabre
Apr 22, 4:40pm
Applied Mathematics - Seminar - E1 025
Wed.
Apr 22

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