Download Ingrid Daubechies - Department of Mathematical and Statistical

Ingrid Daubechies (DOBE-UH-SHEE)
Born/Died- August 17, 1954
Location- Houthalen, Helchteren Belgium
Famous forFamous physicist and mathematician
Best known for her work with wavelets in image compression
(wavelets and their applications)
Currently works as professor in the mathematics and applied
mathematics department at Princeton University.
• Jan. 2011 move to Duke University.
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Defining moment• Played with dolls and loved to sew clothes for them, flat pieces of
fabric would be not flat anymore, but follow curves
• As a child- Laying in bed adding 1+1=2, 2+2=4, 4+4=8, and realizing
how the numbers add up to big so quickly
• As a teen, realizing that by adding
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Why did I do mathematics• Began studying physics and then changed to mathematics
Explain my mathematics-
• Discovered the construction of compactly supported continuous
wavelets
Famous mathematicians I encounted with-
• 1985 met mathematician Robert Calderbank (married in 1987)
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Cool interesting story about me• Many awards- Louis Empain Prize for Physics (1984), American
Mathematical Social Steele Prize for her wavelet book (1994)
• Emmy Noether Lecturer (2006), United States National Academy of
Sciences Award in Mathematics (2000), MacArthur Fellowship (1993)
• First woman to receive the National Academy of Sciences Award in
Mathematics, awards for excellent published mathematical research,
etc.
Matimatician I admired the most- not described
Education• 1975 undergraduate in physics at Vrije University
• 1980 PH.D in theoretical physics
Family• Marcel Daubechies, a civil mining engineer
• Simonne Duran, a criminologist
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Sources:
http://en.wikipedia.org/wiki/Ingrid_Daubechies
http://www.pacm.princeton.edu/~ingrid/vitae/bio.html
http://www.math.princeton.edu/~ingrid/personalbio.html
The concept of wavelets has its origins in many fields, and part of the accomplishment of Daubechies is finding
those places where the concept arose and showing how all the approaches relate to one another. The use of
wavelets as an analytical tool is like Fourier analysis - simple and yet very powerful. In fact, wavelets are an
extension of Fourier analysis to the case of localization in both frequency and space. And like Fourier analysis,
it has both a theoretical side and practical importance. ...
Ian Stewart has described a number of applications of wavelet analysis. Here are two such descriptions:Perhaps the best-known application of wavelet analysis to date derived from the U.S. FBI's decision in 1993 to
use a wavelet transform for encoding digitised fingerprint records. A wavelet transform occupies less computer
memory than conventional methods for image storage, and its use was predicted to reduce the amount of
computer memory needed for fingerprint records by 93 %. ...
In the past two decades, medical centres had come to employ various kinds of scanner-based imaging systems,
such as computed tomography and magnetic resonance imaging, that use computers to assemble the digitised
data collected by the scanner into two- or three-dimensional pictures of the body's internal structures. ... a poor
digitised image can be smoothed and cleaned up by taking a wavelet transform of it, removing unwanted
components, and "detransforming" the wavelet representation to yield an image again. The method reduced the
time of the patient's exposure to the radiation involved in the scanning process and thus made the imaging
technique cheaper, quicker, and safer.