I have the data from somewhere then I have to solve the problem.

I have to translate this kind of knowledge into a different language,

in my case into mathematical language.

Then I going to try to solve this problem,

like with my toolset, in this case mathematical toolsets we have learned.

Like in calculus and algebra, whatever it is,

like in this case, and then we come up with a solution.

And then we have a solution which might make a lot of sense to me as

a mathematician,

might make no sense whatsoever like to your interdisciplinary partners.

So, now, it means like you have to translate it back into the other language

and tell them like, well, this is what I found.

This is like my solution to the problem and this is pathway like of the reflection

by looking backstage light, which is always important.

And discuss the solution may we come up like the new strategy

if your partners like your biologist you might think no this is not what it is.

Actually like you found a wrong solution to the problem or

let's be more optimistic, might a level that's a great idea.

And now that solution might make sense let's check it like we've

readjusting or reevaluating data or trying to get new data.

And so this is an ongoing feedback loop

which actually drives everything in nature, I think, not just in mathematics.

But this is how we should actually always aim toward solving a problem.

It's not a one step process where you go forward,

you find a solution and that's it.

No, you go back, readjust and reevaluate,

find maybe a better strategy and then work it through.

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