Please, Professor Stadler, the screen is yours.

We are looking forward to your talk.

OK, thanks so much.

So this is a joint work with Shanin Ton.

She's my PhD student.

She's about to graduate, actually,

and Eric Van Den Eiden, who is a colleague,

who has a lot of experience in rear vent simulations

and those elastic methods and a lot of these kind of things.

So yeah, so I'll get into it.

So what I'm going to do is I'm actually

going to use an application that I think is interesting.

And I'm going to try to develop a little bit of the math

in high level, right?

And then I'm going to talk about the application.

It has to do with tsunamis.

So it's going to involve what's called the shallow water

equation, which is like way too much to tsunami waves.

So that's my plan.

So I'm first going to talk about the math side of this a little

bit, a couple of slides.

So what this whole thing is about

is that I have a function f that maps from some possibly

high dimensional space.

And then this space that has a density,

has a probability, it's a probability space,

in terms of status.

So high dimensional, possibly high dimensional random

variables into the reals.

So it's a function.

And this f in general can be quite complex.

In my case, it's going to involve the solution of the p.

So at the end, we're going to bring in,

I need methods from what's called pd constraint

organization and these kind of things.

So this is the mapping.

And I'm interested in maps into r.

And I'm interested in finding those status for which

this image, this r, is actually very large.

So why is that?

So why is that?

I'm interested in estimating the measure of outcomes

of this mapping that are unlikely, that are rare.

That's why they are rare or slash extreme.

So I'm interested in finding, in saying something

about the set of the measure of this set omega of c.

That's all the parameters theta.

Where f of theta, this complicated map,

is larger than set.

And you can imagine if set gets larger and larger,