How we use astrophysics to study earthbound problems

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
or 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)