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I get out of bed for two reasons.

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One, small-scale family farmers
need more food.

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It's crazy that in 2019
farmers that feed us are hungry.

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And two, science needs to be
more diverse and inclusive.

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If we're going to solve
the toughest challenges on the planet,

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like food insecurity for the millions
living in extreme poverty,

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it's going to take all of us.

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I want to use the latest technology

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with the most diverse
and inclusive teams on the planet

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to help farmers have more food.

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I'm a computational biologist.

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I know -- what is that
and how is it going to help end hunger?

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Basically, I like computers and biology

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and somehow,
putting that together is a job.

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(Laughter)

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I don't have a story

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of wanting to be a biologist
from a young age.

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The truth is, I played
basketball in college.

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And part of my financial aid package
was I needed a work-study job.

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So one random day,

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I wandered to the nearest building
to my dorm room.

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And it just so happens
it was the biology building.

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I went inside and looked at the job board.

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Yes, this is pre-the-internet.

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And I saw a three-by-five card

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advertising a job
to work in the herbarium.

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I quickly took down the number,

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because it said "flexible hours,"

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and I needed that to work around
my basketball schedule.

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I ran to the library
to figure out what an herbarium was.

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(Laughter)

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And it turns out

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an herbarium is where they store
dead, dried plants.

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I was lucky to land the job.

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So my first scientific job

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was gluing dead plants onto paper
for hours on end.

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(Laughter)

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It's so glamorous.

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This is how I became
a computational biologist.

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During that time,

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genomics and computing were coming of age.

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And I went on to do my masters

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combining biology and computers.

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During that time,

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I worked at Los Alamos National Lab

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in the theoretical biology
and biophysics group.

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And it was there I had my first encounter
with the supercomputer,

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and my mind was blown.

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With the power of supercomputing,

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which is basically thousands
of connected PCs on steroids,

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we were able to uncover the complexities
of influenza and hepatitis C.

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And it was during this time
that I saw the power

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of using computers
and biology combined, for humanity.

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And I wanted this to be my career path.

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So, since 1999,

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I've spent the majority
of my scientific career

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in very high-tech labs,

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surrounded by really expensive equipment.

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So many ask me

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how and why do I work
for farmers in Africa.

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Well, because of my computing skills,

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in 2013, a team of East African scientists

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asked me to join the team
in the plight to save cassava.

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Cassava is a plant whose leaves and roots
feed 800 million people globally.

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And 500 million in East Africa.

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So that's nearly a billion people

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relying on this plant
for their daily calories.

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If a small-scale family farmer
has enough cassava,

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she can feed her family

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and she can sell it at the market
for important things like school fees,

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medical expenses and savings.

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But cassava is under attack in Africa.

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Whiteflies and viruses
are devastating cassava.

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Whiteflies are tiny insects

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that feed on the leaves
of over 600 plants.

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They are bad news.

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There are many species;

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they become pesticide resistant;

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and they transmit hundreds
of plant viruses

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that cause cassava brown streak disease

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and cassava mosaic disease.

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This completely kills the plant.

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And if there's no cassava,

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there's no food or income
for millions of people.

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It took me one trip to Tanzania

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to realize that these women
need some help.

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These amazing, strong,
small-scale family farmers,

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the majority women,

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are doing it rough.

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They don't have enough food
to feed their families,

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and it's a real crisis.

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What happens is

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they go out and plant fields of cassava
when the rains come.

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Nine months later,

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there's nothing, because of these
pests and pathogens.

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And I thought to myself,

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how in the world can farmers be hungry?

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So I decided to spend
some time on the ground

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with the farmers and the scientists

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to see if I had any skills
that could be helpful.

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The situation on the ground is shocking.

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The whiteflies have destroyed the leaves
that are eaten for protein,

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and the viruses have destroyed the roots
that are eaten for starch.

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An entire growing season will pass,

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and the farmer will lose
an entire year of income and food,

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and the family will suffer
a long hunger season.

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This is completely preventable.

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If the farmer knew

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what variety of cassava
to plant in her field,

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that was resistant
to those viruses and pathogens,

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they would have more food.

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We have all the technology we need,

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but the knowledge and the resources

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are not equally distributed
around the globe.

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So what I mean specifically is,

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the older genomic technologies

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that have been required
to uncover the complexities

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in these pests and pathogens --

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these technologies were not made
for sub-Saharan Africa.

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They cost upwards of a million dollars;

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they require constant power

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and specialized human capacity.

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These machines are few
and far between on the continent,

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which is leaving many scientists
battling on the front lines no choice

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but to send the samples overseas.

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And when you send the samples overseas,

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samples degrade, it costs a lot of money,

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and trying to get the data back
over weak internet

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is nearly impossible.

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So sometimes it can take six months
to get the results back to the farmer.

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And by then, it's too late.

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The crop is already gone,

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which results in further poverty
and more hunger.

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We knew we could fix this.

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In 2017,

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we had heard of this handheld,
portable DNA sequencer

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called an Oxford Nanopore MinION.

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This was being used
in West Africa to fight Ebola.

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So we thought:

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Why can't we use this
in East Africa to help farmers?

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So, what we did was we set out to do that.

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At the time, the technology was very new,

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and many doubted we could
replicate this on the farm.

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When we set out to do this,

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one of our "collaborators" in the UK

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told us that we would never
get that to work in East Africa,

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let alone on the farm.

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So we accepted the challenge.

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This person even went so far as to bet us
two of the best bottles of champagne

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that we would never get that to work.

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Two words:

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pay up.

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(Laughter)

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(Applause)

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Pay up, because we did it.

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We took the entire high-tech molecular lab

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to the farmers of Tanzania,
Kenya and Uganda,

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and we called it Tree Lab.

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So what did we do?

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Well, first of all,
we gave ourselves a team name --

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it's called the Cassava Virus
Action Project.

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We made a website,

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we gathered support from the genomics
and computing communities,

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and away we went to the farmers.

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Everything that we need for our Tree Lab

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is being carried by the team here.

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All of the molecular and computational
requirements needed

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to diagnose sick plants is there.

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And it's actually all
on this stage here as well.

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We figured if we could get the data
closer to the problem,

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and closer to the farmer,

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the quicker we could tell her
what was wrong with her plant.

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And not only tell her what was wrong --

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give her the solution.

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And the solution is,

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burn the field and plant varieties

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that are resistant to the pests
and pathogens she has in her field.

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So the first thing that we did
was we had to do a DNA extraction.

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And we used this machine here.

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It's called a PDQeX,

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which stands for
"Pretty Damn Quick Extraction."

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(Laughter)

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I know.

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My friend Joe is really cool.

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One of the biggest challenges
in doing a DNA extraction

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is it usually requires
very expensive equipment,

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and takes hours.

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But with this machine,

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we've been able to do it in 20 minutes,

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at a fraction of the cost.

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And this runs off of a motorcycle battery.

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From there, we take the DNA extraction
and prepare it into a library,

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getting it ready to load on

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to this portable, handheld
genomic sequencer,

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which is here,

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and then we plug this
into a mini supercomputer,

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which is called a MinIT.

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And both of these things are plugged
into a portable battery pack.

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So we were able to eliminate

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the requirements
of main power and internet,

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which are two very limiting factors
on a small-scale family farm.

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Analyzing the data quickly
can also be a problem.

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But this is where me being
a computational biologist came in handy.

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All that gluing of dead plants,

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and all that measuring,

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and all that computing

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finally came in handy
in a real-world, real-time way.

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I was able to make customized databases

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and we were able to give the farmers
results in three hours

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versus six months.

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(Applause)

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The farmers were overjoyed.

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So how do we know
that we're having impact?

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Nine moths after our Tree Lab,

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Asha went from having
zero tons per hectare

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to 40 tons per hectare.

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She had enough to feed her family

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and she was selling it at the market,

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and she's now building a house
for her family.

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Yeah, so cool.

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(Applause)

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So how do we scale Tree Lab?

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The thing is,

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farmers are scaled already in Africa.

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These women work in farmer groups,

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so helping Asha actually helped
3,000 people in her village,

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because she shared the results
and also the solution.

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I remember every single
farmer I've ever met.

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Their pain and their joy

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is engraved in my memories.

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Our science is for them.

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Tree Lab is our best attempt
to help them become more food secure.

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I never dreamt

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that the best science
I would ever do in my life

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would be on that blanket in East Africa,

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with the highest-tech genomic gadgets.

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But our team did dream

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that we could give farmers answers
in three hours versus six months,

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and then we did it.

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Because that's the power
of diversity and inclusion in science.

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Thank you.

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(Applause)

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(Cheers)