I get out of bed for two reasons.
One, small-scale family farmers need more food.
It's crazy that in 2019 farmers that feed us are hungry.
And two, science needs to be more diverse and inclusive.
If we're going to solve the toughest challenges on the planet,
like food insecurity for the millions living in extreme poverty,
it's going to take all of us.
I want to use the latest technology
with the most diverse and inclusive teams on the planet
to help farmers have more food.
I'm a computational biologist.
I know -- what is that and how is it going to help end hunger?
Basically, I like computers and biology
and somehow, putting that together is a job.
(Laughter)
I don't have a story
of wanting to be a biologist from a young age.
The truth is, I played basketball in college.
And part of my financial aid package was I needed a work-study job.
So one random day,
I wandered to the nearest building to my dorm room.
And it just so happens it was the biology building.
I went inside and looked at the job board.
Yes, this is pre-the-internet.
And I saw a three-by-five card
advertising a job to work in the herbarium.
I quickly took down the number,
because it said "flexible hours,"
and I needed that to work around my basketball schedule.
I ran to the library to figure out what an herbarium was.
(Laughter)
And it turns out
an herbarium is where they store dead, dried plants.
I was lucky to land the job.
So my first scientific job
was gluing dead plants onto paper for hours on end.
(Laughter)
It's so glamorous.
This is how I became a computational biologist.
During that time,
genomics and computing were coming of age.
And I went on to do my masters
combining biology and computers.
During that time,
I worked at Los Alamos National Lab
in the theoretical biology and biophysics group.
And it was there I had my first encounter with the supercomputer,
and my mind was blown.
With the power of supercomputing,
which is basically thousands of connected PCs on steroids,
we were able to uncover the complexities of influenza and hepatitis C.
And it was during this time that I saw the power
of using computers and biology combined, for humanity.
And I wanted this to be my career path.
So, since 1999,
I've spent the majority of my scientific career
in very high-tech labs,
surrounded by really expensive equipment.
So many ask me
how and why do I work for farmers in Africa.
Well, because of my computing skills,
in 2013, a team of East African scientists
asked me to join the team in the plight to save cassava.
Cassava is a plant whose leaves and roots feed 800 million people globally.
And 500 million in East Africa.
So that's nearly a billion people
relying on this plant for their daily calories.
If a small-scale family farmer has enough cassava,
she can feed her family
and she can sell it at the market for important things like school fees,
medical expenses and savings.
But cassava is under attack in Africa.
Whiteflies and viruses are devastating cassava.
Whiteflies are tiny insects
that feed on the leaves of over 600 plants.
They are bad news.
There are many species;
they become pesticide resistant;
and they transmit hundreds of plant viruses
that cause cassava brown streak disease
and cassava mosaic disease.
This completely kills the plant.
And if there's no cassava,
there's no food or income for millions of people.
It took me one trip to Tanzania
to realize that these women need some help.
These amazing, strong, small-scale family farmers,
the majority women,
are doing it rough.
They don't have enough food to feed their families,
and it's a real crisis.
What happens is
they go out and plant fields of cassava when the rains come.
Nine months later,
there's nothing, because of these pests and pathogens.
And I thought to myself,
how in the world can farmers be hungry?
So I decided to spend some time on the ground
with the farmers and the scientists
to see if I had any skills that could be helpful.
The situation on the ground is shocking.
The whiteflies have destroyed the leaves that are eaten for protein,
and the viruses have destroyed the roots that are eaten for starch.
An entire growing season will pass,
and the farmer will lose an entire year of income and food,
and the family will suffer a long hunger season.
This is completely preventable.
If the farmer knew
what variety of cassava to plant in her field,
that was resistant to those viruses and pathogens,
they would have more food.
We have all the technology we need,
but the knowledge and the resources
are not equally distributed around the globe.
So what I mean specifically is,
the older genomic technologies
that have been required to uncover the complexities
in these pests and pathogens --
these technologies were not made for sub-Saharan Africa.
They cost upwards of a million dollars;
they require constant power
and specialized human capacity.
These machines are few and far between on the continent,
which is leaving many scientists battling on the front lines no choice
but to send the samples overseas.
And when you send the samples overseas,
samples degrade, it costs a lot of money,
and trying to get the data back over weak internet
is nearly impossible.
So sometimes it can take six months to get the results back to the farmer.
And by then, it's too late.
The crop is already gone,
which results in further poverty and more hunger.
We knew we could fix this.
In 2017,
we had heard of this handheld, portable DNA sequencer
called an Oxford Nanopore MinION.
This was being used in West Africa to fight Ebola.
So we thought:
Why can't we use this in East Africa to help farmers?
So, what we did was we set out to do that.
At the time, the technology was very new,
and many doubted we could replicate this on the farm.
When we set out to do this,
one of our "collaborators" in the UK
told us that we would never get that to work in East Africa,
let alone on the farm.
So we accepted the challenge.
This person even went so far as to bet us two of the best bottles of champagne
that we would never get that to work.
Two words:
pay up.
(Laughter)
(Applause)
Pay up, because we did it.
We took the entire high-tech molecular lab
to the farmers of Tanzania, Kenya and Uganda,
and we called it Tree Lab.
So what did we do?
Well, first of all, we gave ourselves a team name --
it's called the Cassava Virus Action Project.
We made a website,
we gathered support from the genomics and computing communities,
and away we went to the farmers.
Everything that we need for our Tree Lab
is being carried by the team here.
All of the molecular and computational requirements needed
to diagnose sick plants is there.
And it's actually all on this stage here as well.
We figured if we could get the data closer to the problem,
and closer to the farmer,
the quicker we could tell her what was wrong with her plant.
And not only tell her what was wrong --
give her the solution.
And the solution is,
burn the field and plant varieties
that are resistant to the pests and pathogens she has in her field.
So the first thing that we did was we had to do a DNA extraction.
And we used this machine here.
It's called a PDQeX,
which stands for "Pretty Damn Quick Extraction."
(Laughter)
I know.
My friend Joe is really cool.
One of the biggest challenges in doing a DNA extraction
is it usually requires very expensive equipment,
and takes hours.
But with this machine,
we've been able to do it in 20 minutes,
at a fraction of the cost.
And this runs off of a motorcycle battery.
From there, we take the DNA extraction and prepare it into a library,
getting it ready to load on
to this portable, handheld genomic sequencer,
which is here,
and then we plug this into a mini supercomputer,
which is called a MinIT.
And both of these things are plugged into a portable battery pack.
So we were able to eliminate
the requirements of main power and internet,
which are two very limiting factors on a small-scale family farm.
Analyzing the data quickly can also be a problem.
But this is where me being a computational biologist came in handy.
All that gluing of dead plants,
and all that measuring,
and all that computing
finally came in handy in a real-world, real-time way.
I was able to make customized databases
and we were able to give the farmers results in three hours
versus six months.
(Applause)
The farmers were overjoyed.
So how do we know that we're having impact?
Nine moths after our Tree Lab,
Asha went from having zero tons per hectare
to 40 tons per hectare.
She had enough to feed her family
and she was selling it at the market,
and she's now building a house for her family.
Yeah, so cool.
(Applause)
So how do we scale Tree Lab?
The thing is,
farmers are scaled already in Africa.
These women work in farmer groups,
so helping Asha actually helped 3,000 people in her village,
because she shared the results and also the solution.
I remember every single farmer I've ever met.
Their pain and their joy
is engraved in my memories.
Our science is for them.
Tree Lab is our best attempt to help them become more food secure.
I never dreamt
that the best science I would ever do in my life
would be on that blanket in East Africa,
with the highest-tech genomic gadgets.
But our team did dream
that we could give farmers answers in three hours versus six months,
and then we did it.
Because that's the power of diversity and inclusion in science.
Thank you.
(Applause)
(Cheers)