Abelforelesningene 2017

24. mai holder Abelprisvinner Yves Meyer sin prisforelesning på Universitetet i Oslo. Deretter blir det Abelforelesninger ved Stéphane Mallat, Ingrid Daubechies og Emmanuel Jean Candès. Forelesningene finner sted i Georg Sverdrups hus.

Se direkte: 

Kaffe og te blir servert fra klokka 9:30 utenfor Auditorium 1.

10:00 - 10:10
Velkomstord ved viserektor ved UiO Knut Fægri, Akademiets preses Ole M. Sejersted og leder av Abelkomiteen John Rognes.

10:10 - 11.05
Abelprisvinner Yves Meyer, École Normale Supérieure Paris-Saclay:
Detection of gravitational waves and time-frequency wavelets

Sergey Klimenko designed the algorithm  used to detect gravitational waves. This algorithm depends on the time-frequency wavelets which have been elaborated by Ingrid Daubechies, Stéphane Jaffard, and Jean-Lin Journé. After describing the now famous discovery of gravitational waves the focus will be on time-frequency analysis.

11:05 - 12:00
Professor Stéphane Mallat, École Normale Supérieure:
A Wavelet Zoom to Analyze a Multiscale World

Complex physical phenomena, signals and images involve structures of very different scales. A wavelet transform operates as a zoom, which simplifies the analysis by separating local variations at different scales. Yves Meyer found wavelet orthonormal bases having better properties than Fourier bases to characterize local properties of functions, physical measurements and signals. This discovery created a major scientific catalysis, which regrouped physicists, engineers and mathematicians, leading to a coherent theory of multiscale wavelet decompositions with a multitude of applications.

This lecture will explain the construction of Meyer wavelet bases and their generalization with fast computations. We shall follow the path of this human adventure, with ideas independently developed by scientists working in quantum physics, geophysics, image and signal processing but also neurophysiology of perception. The synthesis in the 1980's provoked by Yves Meyer's work was an encounter between applications and a pure harmonic analysis research program, initiated by Littlewood-Paley in the 1930's. It remains at the roots of open  mathematical problems in high-dimension, for physics and big data analysis.

13:00 - 13:55
Professor Ingrid Daubechies, Duke University:
Wavelet bases: roots, surprises and applications

Yves Meyer's surprising construction of orthonormal bases consisting of dilates and translates of a single smooth function was followed soon after by the development of the Multiresolution Analysis framework in collaboration with Stephane Mallat. As already shown in the presentation by Stephane Mallat, this development was rooted in and used insights from a variety of fields -- ranging from pure harmonic analysis to statistics, quantum physics, geophysics and computer vision. The lecture will discuss some of those diverse roots in more detail, and also show how the new wavelet synthesis, sparked by Yves Meyer's seminal work, led to further progress in all those fields as well as others. Finally, hindsight shows that the new paradigm introduced by wavelet analysis was a first example of the power of  sparse decompositions -- and thus a prelude to another paradigm shift, that of Compressed Sensing, about which more will follow, in the presentation by Emmanuel Candès.

  • 13:55 - 14:30        Kaffe / te

14:30 - 15:25
Professor Emmanuel Jean Candès, Stanford University:
Wavelets, sparsity and its consequences

Soon after they were introduced, it was realized that wavelets offered representations of signals and images of interest that are far more sparse than those offered by more classical representations; for instance, Fourier series. Owing to their increased spatial localization at finer scales, wavelets prove to be better adapted to represent signals with discontinuities or transient phenomena because only a few wavelets actually interact with those discontinuities. It turns out that sparsity has extremely important consequences and this lecture will briefly discuss three vignettes. First, enhanced sparsity yields the same quality of approximation with fewer terms, a feat which has implications for lossy image compression since it roughly says that fewer bits are needed to achieve the same distortion. Second, enhanced sparsity yields superior statistical accuracy since there are fewer degrees of freedom or parameters to estimate. This gives scientists better methods to tease apart the signal from the noise. Third, enhanced sparsity has important consequences for data acquisition itself: a new technique known as compressed sensing is turning a few fields a bit upside down for it effectively says that to make a high-resolution image we need to collect far fewer samples than were thought necessary. 

15:25 - 15:30
Avlsluttende bemerkninger ved Abelstyrets leder Kristian Ranestad

Praktisk informasjon

24 Mai 2017
10:00 - 15:30

Georg Sverdrups Hus, Universitetet i Oslo

Meld deg på lunsjen her


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