Hello all,
I wanted to share with everyone that Julia won the 2019 James H. Wilkinson Prize for Numerical Software. This recognition is a great milestone for our community and it is a nice note to start the next year on. 
Here’s the MIT press release:
This is the page for the prize itself (which will be updated closer to the time the actual prize is awarded at SIAM CSE):
-viral
Congratulations! It was well deserved! Here in Brazil (INPE), Julia opened a huge amount of possibilities to analyze space missions, create satellite simulations, use evolutionary algorithms to search for optimal solutions for spacecraft design, etc. This is something I would not be able to do using other languages with so little effort.
Congratulations to all the Julia team for your awesome work 
Congratulations for this prestigious award!!! 
This is awesome! I actually have a package named Wilkinson which I have not registered yet (because the name NumericalAnalysis was too broad). I wrote a paper for the AES Julia special edition in which I proved a new generalized Wilkinson-type numerical error bound and used Julia for the calculations, which is why I changed the package name to Wilkinson. I waited to change the name because I didn’t want to confuse the reviewers of the article, but now seems like a good time to follow through with the name change:
Cool that you won the Wilkinson prize, didn’t know such a thing exists when I started this paper / package.
Since it’s not so obvious what the Wilkinson package does from the current README, here is an example. The new generalized Wilkinson-type error bound I proved in my paper allows the prediction of a round off error bound for any type of polynomial form (previously only the Horner form and Basis forms have been investigated). Here is what this looks like with actual data generated with Wilkinson in Julia
Shown below is the predicted error bound for the round off error of a polynomial, and also the actual round off error computed using polynomial evaluation normalized by arbitrary precision float for comparison.
Using the table below, I am able to make predictions about what is the most optimal form of a polynomial, satisfying both the least Wilkinson-type error bound and also minimal memory usage, etc.
The main result is a new characteristic method, which is able to predict the ordering of the polynomial forms in terms of the generalized Wilkinson-type round off error bound, which is enabled by Julia AST.
This research discovery would not have happened if Julia did not exist, because then I wouldn’t have been messing around with this kind of thing using the Julia abstract syntax tree (AST) representations.
You can read a draft of the paper here: GitHub - chakravala/Wilkinson.jl: Toolkit for studying numerical analysis and floating point algebra round-off
So, thank you for the Julia AST, which led me to this discovery, which is also related to James H. Wilkinson, whom I also referenced in my paper.
Congratulations to you all. And many thanks for creating the Julia language. I haven’t had so much fun programming since I submitted my first batch of punch cards of Fortran IV!
Awesome! Nobody deserves it more than you. I always tell people, “Julia is the language I always wanted to create, but never could have.” You guys should be so proud of what you all have accomplished. The award just serves to confirm what we here already knew: Julia is an extraordinary achievement.
Can we read about this in more details somewhere?
Hi @sairus7,
There is one paper describing the simulator that was built:
https://journals.sagepub.com/doi/full/10.1177/1063293X18804006
A post in Julia Computing website:
https://juliacomputing.com/case-studies/BrazilNationalinstituteforspaceResearch.html
And the two packages I registered that are used in all the tools:
Of course, fell free to PM me about anything you want to know
Congrats from my side as well. KISS: thank you for Julia !
Wow! Congratulations on this much, much deserved award!
