Hacker Remix

Ingrid Daubechies awarded National Medal of Science

185 points by sedtacet 1 week ago | 14 comments

szvsw 1 week ago

Daubechies wavelets are such incredibly strange and beautiful objects, particularly for how deviant they are compared to everything you are typically familiar with when you are starting your signal processing journey… if it’s possible for a mathematical construction to be punk, then it would be the Daubechies wavelets.

_kb 1 week ago

For those unfamiliar with her work, there's a very approachable lecture on wavelets and their common applications here: https://www.youtube.com/watch?v=a90kMHY0Uto.

anyfoo 1 week ago

Thank you for that, I was looking for exactly this. I consider myself fairly competent in a bunch of DSP topics (Fourier and Laplace, and respectively the z-transform, are no mystery to me), but I have a few problems to solve where I feel that wavelets could be very beneficial.

kkylin 1 week ago

Here is the full list of this year's awardees:

https://new.nsf.gov/honorary-awards/national-medal-science#n...

(Also includes National Medal of Technology and Innovation.)

And if you ever get the chance to hear Daubechies speak, go! She gives very clear and accessible talks, and is also very approachable.

cosmic_quanta 1 week ago

This award is well-deserved!

I was inspired by her work in the 2010s and have since used the wavelets to denoise time-series with great success [0]. I believe that learning about wavelet transforms is both beneficial in itself, but also beneficial in understand the ubiquitous Fourier transform.

[0]: https://laurentrdc.xyz/posts/wavelet-filtering.html

331c8c71 3 days ago

I checked your post and it is not clear to me what is the added value of wavelets in the setting you used to illustrate.

The noise was i.i.d. random normal variates with the mean of -0.5 which is exactly like shifting the signal by -0.5 and then adding zero-centered noise. Well, let's say you shift by -10 or -1000 instead - there's no way to recover the magnitude of that shift unless one has additionnal information (like the true signal should be zero mean for instance).