How we use astrophysics to study earthbound problems | Federica Bianco

Translator: Reviewer: Daban Q. Jaff

I am an astrophysicist.

I research stellar explosions across the universe.

But I have a flaw:

I'm restless, and I get bored easily.

And although as an astrophysicist, I have the incredible opportunity

to study the entire universe,

the thought of doing only that, always that,

makes me feel caged and limited.

What if my issues with keeping attention and getting bored

were not a flaw, though?

What if I could turn them into an asset?

An astrophysicist cannot touch or interact with

the things that she studies.

No way to explode a star in a lab to figure out why or how it blew up.

Just pictures and movies of the sky.

Everything we know about the universe,

from the big bang that originated space and time,

to the formation and evolution of stars and galaxies,

to the structure of our own solar system,

we figured out studying images of the sky.

And to study a system as complex as the entire universe,

astrophysicists are experts at extracting simple models and solutions

from large and complex data sets.

So what else can I do with this expertise?

What if we turned the camera around towards us?

At the Urban Observatory, that is exactly what we are doing.

Greg Dobler, also an astrophysicist

and my husband,

created the first urban observatory in New York University in 2013,

and I joined in 2015.

Here are some of the things that we do.

We take pictures of the city at night

and study city lights like stars.

By studying how light changes over time

and the color of astronomical lights,

I gain insight about the nature of exploding stars.

By studying city lights the same way,

we can measure and predict how much energy the city needs and consumes

and help build a resilient grid

that will support the needs of growing urban environments.

In daytime images, we capture plumes of pollution.

Seventy-five percent of greenhouse gases in New York City

come from a building like this one, burning oil for heat.

You can measure pollution with air quality sensors.

But imagine putting a sensor on each New York City building,

reading in data from a million monitors.

Imagine the cost.

With a team of NYU students, we built a mathematical model,

a neural network that can detect and track these plumes

over the New York City skyline.

We can classify them --

harmless steam plumes, white and evanescent;

polluting smokestacks, dark and persistent --

and provide policy makers with a map of neighborhood pollution.

This cross-disciplinary project created transformational solutions.

But the data analysis methodologies we use in astrophysics

can be applied to all sorts of data,

not just images.

We were asked to help a California district attorney

understand prosecutorial delays in their jurisdiction.

There are people on probation or sitting in jail,

awaiting for trial sometimes for years.

They wanted to know what kind of cases dragged on,

and they had a massive data set to explore to understand it,

but didn't have the expertise

or the instruments in their office to do so.

And that's where we came in.

I worked with my colleague, public policy professor Angela Hawken,

and our team first created a visual dashboard

for DAs to see and better understand the prosecution process.

But also, we ourselves analyzed their data,

looking to see if the duration of the process

suffered from social inequalities in their jurisdiction.

We did so using methods

that I would use to classify thousands of stellar explosions,

applied to thousands of court cases.

And in doing so,

we built a model that can be applied to other jurisdictions

who are willing to explore their biases.

These collaborations between domain experts and astrophysicists

created transformational solutions

to help improve people's quality of life.

But it is a two-way road.

I bring my astrophysics background to urban science,

and I bring what I learn in urban science back to astrophysics.

Light echoes:

the reflections of stellar explosions onto interstellar dust.

In our images, these reflections appear as white, evanescent, moving features,

just like plumes.

I am adapting the same models that detect plumes in city images

to detect light echoes in images of the sky.

By exploring the things that interest and excite me,

reaching outside of my domain,

I did turn my restlessness into an asset.

We, you, all have a unique perspective that can generate new insight

and lead to new, unexpected, transformational solutions.

Thank you.

(Applause)