(synthesizer sound effect)
- Welcome everyone.
Families, loved ones and friends.
I present to you the 2018, 2019
UC Berkeley Letters in Science
Data Science Graduating Class.
(audience cheers and applauds)
Welcome to our students,
welcome to our faculty,
our campus leadership.
Our honored guests and
our extraordinary staff.
I am Cathryn Carson,
faculty leader of the Data
Science Education Program.
Some of our graduates
know me as an historian.
In fact I've taught some
of you, many of you,
about the human stories of data.
Its social and human context and ethics.
And as a historian it is a thrill
to see our paths converging
on this special day.
Our paths and our stories,
because in coming together today
we are weaving a new story
in which we are all authors
whose outcome we have the power
and the responsibility to define.
Our graduates first of all.
Your stories have followed
many through lines,
they've drawn you into data
science from many backgrounds,
many orientations,
many sets of family and friends.
If we judged by your domain emphases,
the areas each of you chose
as a real-world specialization
for your studies,
You are excited about
evolution and biodiversity,
economics, physical sciences,
robotics, organizations and the economy.
Social policy and law,
sustainable development and engineering,
linguistics science, cognition.
Business and industrial analytics.
And that's less than half
of the domain emphases you've chosen.
You've merged that inbound curiosity
with your technical courses.
After the sheer excitement of Data 8,
the Foundations of Data Science.
You've used your personal
interest to motivate you
to gain a strong grounding in probability,
data structures, algorithms, inference.
Or you've taken pleasure in those topics
for their very own sake,
as you've come to appreciate
their beauty and depth.
And you've woven together
your own identities,
positionalities and personal values
with the ethical and societal choices of
this datafied world you
are helping to build.
All of this brings you together
as a distinctively Berkeley class
of data science graduates to be.
And then you didn't just
take data science courses,
you helped create them and teach them.
You have already woven your stories
into each other's and ours.
When we asked you about your
experience in the major,
you told us about service.
I'll read a few.
I started working one graduate said,
for the Division of Data Science
before it was even
officially the division.
When I realized I would
be able to complete
the major requirements
in time to graduate,
it was very exciting.
Another.
My favorite experience in data science
was becoming a UGSI,
that's an undergraduate
teaching assistant.
For my favorite course, Data 8.
Having the opportunity
to give back to the class
that has given me so
much has been incredible.
I love being part of students
first exposure to data science,
and see them learn and gain
interest in the field as a whole
as I had experienced myself.
And other graduate writes.
My most memorable experience
was when I was accepted
to be peer advisor.
I was excited and honored
to be one of the first peer
advisors for a new major.
And then another.
I am honored to be part
of Berkeley's Division of Data Science,
internal external operations team
who built out Data X Online
and helped uplift Berkeley's
data science program
to become available not
only to Berkeley's students,
but to students and
schools around the Bay Area
and in other parts of the country.
And some of our graduates stories
acknowledged the well just slightly
crazy character of this new program.
I quote,
"I enjoyed trying to guess
"what the expected data
science curriculum would be."
So there is a story here too.
A story of weaving a program together
with the dedicated faculty and staff
who are witnessing your
commencement today.
A number of us up here at front
have been on this path for five years.
That was when the design team
for this major was launched.
It has become integral to our
life stories as well as yours.
And across the board,
Berkeley's faculty have invested
incredible creativity,
amazing vision,
and shall I say unsurpassed
fortitude and determination
in creating and teaching the classes
and programs you have passed through.
They are, we are so thrilled to have
the chance to be here with you.
And I don't think there's been any major
that's been served by more
pathbreaking and dedicated staff.
The intrepid advisers who counseled you,
the inventive coordinators
who built programs around you,
the amazing technical team
who created and managed the
infrastructures you used.
I want on behalf of the faculty
to give special acknowledgement
to three staff members who have
so completely interwoven their
own stories with today's commencement.
To Anthony Swen,
who as our very first
data science employee
has helped lead through the wilderness,
and truly done a piece
of just about everything.
When asked if something is possible,
he always says, we'll make it work.
Even when he should say, no.
Then to Carlan Shenen,
who has a title called
Executive Assistant to the Dean,
which really means she who runs everything
with extraordinary
competence behind the scenes.
And to Marjorie Enser,
who as a deeply experienced adviser
has expertly designed
and fluidly orchestrated
today's commencement.
What an accomplishment,
thank you Marjorie and all of the team.
And lastly, the story we're
authoring here together
draws in all of you,
whether on the stage, or
in the auditorium seats.
You have helped make this day possible
In a way that may be obvious.
Chancellor Crist,
Provost Ala Vasadus
Kate Johnson,
or hidden, possibly known only to you.
We are so grateful that each
of you contributed your story
to this first chapter
of the Division of Data
Sciences at Berkeley,
and we look forward to
authoring the next one with you.
So that is our welcome and our thanks.
It is time for the ceremony.
I have the pleasure of introducing
our Dean, David Culler.
(audience applauds)
- What a pleasure.
Chancellor Christ, Keith
Johnson, Provost Alivisatos,
Dean Jacobson,
faculty, family, friends,
thank you for joining us
on this most exciting day.
I know you all join me
in being just so proud
of the first ever Berkeley
graduating class of data science.
In fact seeing that pride well up
throughout this auditorium, all of you,
why don't we let these graduates know
just how proud of them we are?
(audience cheers and applauds)
And why are we so proud?
It's not just that you worked
long and hard with creativity
and thoughtfulness
through a rigorous program
of study to achieve this degree,
although that is all true.
It's also because you are so brave.
You could have chosen a well trodden path
and pursued a degree
that is well established
with a long history behind
it and a clear journey ahead.
Be that into particular jobs,
particular fields of graduate study,
and yes, many of you did that too.
But you have all chosen
to break a new trail,
one that no high school counselor
would have recommended
to you four years ago.
One that you probably never talked about
with your friends and family
before you arrived at Berkeley.
You came to college and
truly met with discovery.
Many of you tell me
stories about the major,
how it just seems to be made for you.
It was.
You are explorers on your own expedition
and this is but one step.
And what is so amazing
about this expedition
is it can lead absolutely anywhere.
How did you become so courageous?
Part of the answer is right behind you.
In fact, graduates,
why don't you stand up, turnaround,
and let them know just
how thankful you are?
(audience applauds)
So what does one say to brave explorers
heading off on an expedition?
Perhaps the words of
Thomas Jefferson in 1803
when he sent Meriweather
Lewis and William Clark
off to find a water passage
from the Missouri to the Pacific Ocean.
Yes, there was a little
problem with that project spec,
like the Rocky Mountains in between.
They weren't just to chart the course,
they were also to catalog all the plants
and animals and minerals along the way,
learn the languages and
cultures of the peoples,
identify diseases and their remedies,
and find a handy ship back home.
It sounds like a list of our
domain emphases doesn't it?
In addition, he gave them guidance,
he said about the people you meet quote,
"Treat them in the most friendly
and conciliatory manner,
"allay all jealousies as to
the object of your journey.
"Satisfy them as to its innocence,
"and confer with them on the points
"most convenient at mutual emporiums
"and articles of the most
desirable interchange."
And of course, they would
not have made it anywhere
without the support of numerous
Native American Nations
they met along the way.
So unless you think you
are the first data science explorers,
consider Jefferson's further guidance.
Your observations are to be taken
with great pains and accuracy
to be entered distinctly and intelligibly
for others as well as
yourself to comprehend.
And with the aid of the usual tables
fix the latitude and longitude
of the places with which they were taken.
Geo code them.
No Geo Sat yet.
Several copies of these,
as well as other notes should
be made at your leisure times
and put in the care of your
most trusty attendants.
A good team, hard to find.
To guard them by multiplying them
against accidental losses to
which they will be exposed.
And he points out,
a further guard would be
that one of these copies be written
on the paper of the birch,
as it is less liable to injury from damp,
than common paper.
So whether it is birch bark,
notebooks, that would be the Jupiter kind,
or elegant computational
narratives and visualizations,
take the tools you've learned,
look carefully all around,
and go make a positive
difference in the world
in your own unique way.
Congratulations.
(audience applauds)
And with that, Professor John De Niro
will introduce our faculty speaker,
and I'm quite sure John De
Niro needs no introduction.
(audience cheers and applauds)
- It is my great honor and pleasure
to introduce today's faculty speaker
on this unique and wonderful event.
Professor Ani Adhikari actually earned
her PhD in statistics right here at Cal,
after a brief exile at
Stanford she did return to Cal
and has been delighting her colleagues
and students ever since.
But she's one of my personal heroes,
and I wanted to share a little bit why
before I give her the microphone.
Before you all knew her,
she invested a ton of energy into Stat 2,
Statistics 2, Introduction to Statistics.
And no computers, no PowerPoint,
she would just grab
chalk and teach students
how to make precise and
proper statistical arguments
without too much jargon
or equations or formulas.
And it was a great course,
She won the Distinguished Teaching Award,
which is Berkeley's
highest honor for teaching.
She created an online course
that I think tens or hundreds
of thousands of students took.
She made her mark on
the world, she did it.
But now we know that she
was just getting started.
Because she and a group of faculty here,
many of whom are in attendance today,
had a vision for a new kind of course,
a way of teaching students
to make proper and precise
statistical arguments,
but with computation
and computer-generated
data visualization as a
central part of the story.
And so, having crafted Stat 2,
into perhaps the best
statistics course on the planet,
Professor Ani Adhikari
signed up to replace it
as the first instructor of Data 8.
So Data 8, Foundations of Data Science
was a new course that was
built as a collaboration.
Many of us worked on it.
But someone had to actually stand up
in front of the students
in that first semester
and deliver all the
lectures, take the reins,
and that was Professor Adhikari,
and I think some of you actually were
in that very first course.
She didn't stop there.
In the last few years she's created
two other probability courses
that follow in the same vein
of using computation
to help students master
reasoning about uncertainty
and impartial information.
She's been up here in front of students
on the Wheeler stage,
she's been behind the scenes
helping craft the structure of the major.
It seems the students have noticed
that she's been making
a lot of contributions,
because this year in the yearly poll
of The Daily Cal,
she was named the best
professor on campus.
Not bad.
(audience cheers and applauds)
So here we are at an event
that probably wouldn't have existed
If it weren't for all her
efforts and brilliance.
Did I use the word probability there okay?
Anyway, we get to hear
from her one last time
which is really wonderful.
So please join me in welcoming
Professor Ani Adhikari.
(audience cheers and applauds)
- You know I'm only following
in Professor De Niro's footsteps,
last year it was he who was voted
the best professor on campus.
(audience applauds)
Thank you Professor Carson and Culler
for giving me the honor of speaking today.
In future years many
students will graduate
with data science degrees
at Berkeley and elsewhere.
But there will be none like this group.
And I am profoundly grateful
for the opportunity to address them.
Because these are the students
who took a chance on us.
On our new courses
on a funny looking
Professor with a funny name,
and on the new major.
Guys, thank you.
I had the privilege of
giving the very first
lectures in Data 8,
and last night I went back
and I dug up my old rosters
from that first year.
2015, 2016,
and I shut my eyes and I
could see all the faces.
And now my eyes are wide
open and I see all the faces.
So many of you who are
seated before us today
who were on those lists.
You who in your freshman year,
trusted your instincts
and enrolled in a course brand-new,
solely on the strength of
the course description.
Because nothing like it
existed anywhere in the world.
On behalf of the faculty,
I want you to know that this
program went from non-existent
to fully fledged major in just four years,
because we were inspired by
your curiosity and your courage.
Not to mention your
sheer dogged persistence
in never letting us forget
that you wished to
graduate with this major,
so would we please get a move on.
(audience cheers and applauds)
But you knew we couldn't
do it without help.
Or maybe you just decided
that if you wanted
something done around here
you were gonna have to do it yourself,
whatever the reason,
you rolled up your sleeves
and you partnered with
us in making this happen.
You taught labs, you taught sections,
you held office hours.
You built infrastructure.
You advised your fellow students.
You helped faculty create
new course materials.
You helped faculty create entire courses.
You helped create the very program
of which you are today
the very first graduates.
In doing so, you transformed education.
And no, that is not an exaggeration,
you know me, you know
that I do not exaggerate.
You helped create an academic program
that has become a model for
universities around the world.
But more importantly,
here is something that you may not know.
I believe you have transformed forever
the relation between faculty
and undergraduate students.
For as long as I can remember
that relation was one
of teacher and student.
The person at the podium
and the person there.
But you have been so much
more than our students.
You have been our co-workers,
our collaborators,
you have been our colleagues.
Your maturity, sense of responsibility,
dedication and creativity,
have been a revelation to faculty.
The impact of our collaborative work,
and the richness of the
experience for the faculty,
and I hope also for you,
are sure signs that this
change is irreversible.
Never again will we the faculty
think of undergraduates only as students.
They will always be our valued teammates.
This is the single biggest change
to undergraduate education
that I have seen in my time as a teacher.
And it is you who made it happen.
Now, when it comes to
data science education,
it has been apparent for a while
that what Berkeley does today,
other universities do next year.
So soon enough the same
change will come about
at other campuses across the country.
Faculty will collaborate
with undergraduates
as well as teach them.
Now, when I was young,
disruptive students had
to go stand in the corner
until they got back with the program.
But these days disruption
is a badge of honor,
so wear it with pride,
because you have disrupted
undergraduate education.
And now as you prepare to leave Berkeley,
I have three requests.
First, never lose that
spirit that you have
that makes wonderful new things happen.
The world is changing
at a bewildering pace.
Five years from now people you
will have jobs that we
can't even imagine today.
People will need skills that
haven't been invented yet.
That is why the world
needs leaders like you.
You could have chosen to take a well known
and thoroughly established class,
but instead you chose to forge
a new path and take Data 8.
You are nimble in mind and in action,
you know how to jump in
and learn new skills.
You can spot interesting new avenues
and you are not afraid to explore them,
the world needs you.
Second, I hope that
you will keep listening
just as you do now deeply and thoughtfully
to others who are not exactly like you,
who do not share the same views.
You could have chosen a major
in which the student body
is rather homogeneous,
but instead, you choose data science.
Where your fellow students
have myriad different backgrounds,
and perspectives and goals.
You have acquired skills in
several different domains,
not just one or two.
You didn't just listen to what you
were already comfortable
hearing and shut out all else.
Instead, you work together in teams
of people with complementary skills.
Because of you data science at Berkeley
is not just a major,
it is a community.
The world needs leaders like you
who actively seek out diverse viewpoints,
to understand nuance and uncertainty.
Who consider the human
consequences of their decisions
beyond their own immediate circle,
the world needs you.
Finally, respect the data.
You could have chosen any other major,
but instead you majored in data science.
In so doing, you took a public position.
The name of your major, data science,
is your proclamation that you believe
in reasoning based on evidence.
You will even have a degree in it,
which is more than the rest of us can say.
And oh how the world needs you.
In this era of alternative facts
and casual disregard for evidence
if it contradicts opinion,
you the data scientist are
the ultimate superhero.
Your superpower is integrity.
Protect the world from selective
and biased appeals to data,
and all such 21st-century spin doctoring
that masquerades as data analysis.
Use what you have learned
over the past four years
to make all of your analyses open,
well-founded and transparent.
Let the data tell their own story,
and please, bring back the age of reason,
the world needs you.
So, go for it.
Grab the future with both hands.
The faculty can't wait for you to do that,
because we know that then,
the future will be in
the best possible hands.
Thank you in advance for the wonders
that you are about to perform,
and on behalf of of all my
colleagues on the faculty
thank you for the past few years.
Congratulations on your
extraordinary achievements,
and best of luck for the future.
(audience cheers and applauds)
- Thank you Professor Ani Adhikari
for a wonderful wonderful speech.
So I'm Bing Yu from the
statistics department,
I'm honored to present
your undergraduate speaker Kyle Nguyen.
(audience cheers and applauds)
Kyle is a member of the graduating class.
Like all of you he set his
sights on the data science major
well before it became official.
And in 19, no 19, you see, I'm old.
In 2015 when he first started
working with the student team
he would become the future
division data scientist,
he has ever since become a
core member of the community
and as a student youth he
has been hugely influential.
He used his excellent
organizational skills
to distill effective systems
and shape the processes
for recruiting and management students
to maximize their opportunity
to learn and grow.
We'll miss you Kyle,
your capable leadership
and positive energy,
and we look forward to
hearing your experiences
as a data scientist and as
a leader in the real world.
Kyle.
(audience cheers and applauds)
- Well, looks like it's the end
of the road for us here at Berkeley.
At least for the time being.
As we walk away from Berkeley
today, we stand tall,
proud that we are trailblazers
in the realm of data science.
As one of the first data
science programs in the world,
we are setting an example
for others to follow.
It took a lot of courage
from everyone to want to join
and become a part of this
data science movement.
I know a lot of us sitting here today
wanted to become part of the program
even before we knew which classes to take.
What makes this program so great to me
is how our community made
a place for everyone.
Regardless of your background,
what you originally planned on studying,
and even what you wanted to do
with data science afterward,
this programs ultimate goal
was to make sure that everyone belonged.
And for such a young program,
that's quite an accomplishment.
I found this to be really true for myself,
as my four years at Berkeley
has been very much defined and shaped
by my experience being
a part of this program.
Like many others,
the intro class Data 8
was my first foray into data science.
This was during Fall, 15,
the first semester for many of us here,
back when everything
was still hypothetical.
The major and all the
subsequent classes and programs
were all just part of the ambition.
Honestly, I didn't really
know what data science was,
and around the time it
was a pretty common phrase
that started to get thrown around more,
but what did it actually mean?
In Data 8, Professor Ani Adhikari
taught us that data science
was anything that we
wanted to make it to be.
We were given many examples
about how data science could be used
to explain the world around us.
My very first assignment in lab
was figuring out who was
the best basketball player,
and this was back in 2015.
The data showed us Steph Curry.
Four years later they are
about to make the finals again,
and there are probably gonna
get a fourth championship,
so data science is right here.
(audience laughs)
So it quickly became clear
that there was no limit
to the ways we could apply
data science to the world,
and that's why it felt
like a comfortable place
for so many backgrounds to congregate.
The diversity was
absolutely incredible to me,
and we were given the opportunity
to define data science for ourselves.
I loved my experience in Data 8,
and I was really inspired by the faculty
and their grand visions for more classes,
a major and then a department.
So naturally as an ambitious freshman
I wanted a piece of that.
So when there was a call
on our Piazza student form
for students interested in
working with the program,
I jumped at the chance.
Great, I was gonna shoot my
shot and see where I could fit.
And I remember in my very first meeting
with art director Anthony Swain,
I just wrapped up my first semester,
confident I had the skills to
tackle whatever came at me.
Sitting across from Anthony in that room
in the Berkeley Institute for data science
I was getting pretty nervous.
I remember reciting
methods of bootstrapping,
hypotheses testing,
and coming up all the examples
of data science projects that I worked on.
I was in full-fledged interview mode
ready to be completely
grilled about data science
in this interview with someone
so important to the program.
But Anthony didn't ask
me about any of that,
instead he asked me about my ambitions,
he asked me what I really cared about,
and that stuck with me.
That was a really big
sign for things to come.
Throughout the years I
was able to grow a lot,
eventually to become
a student leader here.
My proudest accomplishment working here
is helping Anthony to greatly
expand our research program,
and in the end that helped
hundreds of aspiring
data science students find
an outlet for their interest.
And I can only attribute
this personal growth
and the ability to do these things
from the programs core ethos
of being student centric.
Willing to believe in their students,
and being unafraid to help them develop.
A program for students
where a lot of the components
were built by fellow students.
And I'm proud to say that many people
in the Berkeley community,
major or not,
were able to grow individually so much
as a result of the
efforts of everyone here.
In the final stages of my involvement
I was helping Anthony to coordinate
the division student
teams as project manager,
and I was incredibly humbled
that my path took me here.
Thank you.
That was the semester
when the major was officially announced,
and that was when reality really hit me,
all of our efforts
successfully came together
and my personal data
science journey at Berkeley
came full circle.
Looking back it's incredible to me
that the program at this scale
would put so much faith into its students,
but this ended up being an important part
of our eventual success.
This is why I never hesitate
to call the division
of data science a home
from me here at Berkeley.
In this home I've met so
many great people here
that I'm proud to call my friends,
I have learned so much
in the classes, research,
and most importantly from other
shared diverse experiences,
and I hope you all have as well.
Now that I'm minutes away from getting
my data science degree,
I can finally say that
I have a better idea
of what data science is.
It has undoubtedly altered
my perspective on the world.
I'm much more curious
about how things work
and what I can do to explain them.
And so because of this data science to me
is the ability to explore the world.
It's an amazing feeling to know that after
we leave here today all
of this will continue,
and all of these experiences
will be here for the next
generations of students to come,
because the marathon continues.
Thank you everyone,
to the graduating class of 2018 and 2019,
and congratulations.
(audience applauds)
- Thank you Kyle.
So it's now my honor to introduce
our commencement speaker Kate Johnson,
president of Microsoft US.
Microsoft has been one
of our closest partners
literally from the very first days
of data science at Berkeley.
But that's building on
a trust relationship
that goes back more than 30 years.
But especially today,
as the company seeks to, quote,
to empower every person
in every organization on
the planet to achieve more.
A mission we naturally
embrace here at Berkeley.
And beyond her role in the company,
Kate personifies her
own personal trajectory,
crossing traditional boundaries
that are so intrinsic to data science.
Electrical engineering
as an undergraduate,
on to business at Wharton,
and I learned today,
she's here with you rather than going
to her 25th anniversary
of her graduating class.
(audience applauds)
From there she grew on to leadership roles
throughout the classical
software industry,
Oracle, Red Hat, now Microsoft.
Investment banking.
And really going further
to the role of information technology
and absolutely everything with
her time at General Electric.
With Microsoft's profound shift in mission
from putting a PC in every home
to empowering people,
she stepped into her current role
shaping the technology that underlies
our connected age and
how it comes into being.
Along her remarkable path,
Kate is a leader in transformation,
which makes her particularly well suited
to address this pathbreaking class.
She's been a passionate advocate
for diversity and inclusion,
which is so core to our program.
We are extremely fortunate to have her,
please join me in welcoming Kate Johnson.
(audience applauds)
- Thank you Dean Culler.
Chancellor Chris, Doctor Carson,
parents, siblings,
friends, family members,
and most importantly the
data science class of 2019.
It's my distinct honor
to address the first
data science commencement
in one of the nations first
data science major programs.
At the first university
that took the risk of
inviting me to speak.
(audience laughs)
Last chance Dean Culler, last chance.
We're good to go?
Thank you for letting me share
this special day with you,
your families and this
extraordinary community.
Speaking today it feels like
a really big responsibility I have to say.
I mean, I'm one of the few
things that stands between you
and tonight's series
finale of Game of Thrones.
I know where I stand.
Someone asked me if I was nervous
to speak to such a big crowd today,
and I said why would I be?
It's not like it's the
first day of Data 8.
So no, I'm not nervous,
but I've got to say,
the more I learn about you guys
the more awestruck I become.
All the work you did to
create this brand-new major.
The skills you've gained,
not just by studying data science,
but by teaching it too.
Your ambitious appetite for academics.
I learned that many of you are double
or even triple majors.
And of course the Sway brothers,
they're quadruple majors.
If I tried to finish four majors
I would still be in college.
So many of you have already
accomplished great things.
I heard that one of you
accidentally signed up
for the wrong class and used
the skills you learned there
to write software for NASA
that governments use for climate change.
That should be Berkeley's new motto,
even our mistakes make
the world a better place.
And of course, the most impressive of all,
some of you passed Professor
Sahi's CS189 class.
Right?
And for that you deserve
an extra round of applause.
(audience applauds)
Data science technologies have advanced
light years since I graduated college,
but in many respects,
we are still in the Wild
West of data ethics.
The rules are still being written.
And so the questions
you wrestle with here,
they are not just the most
important ones of our time,
they are going to be
important for a long time.
Questions like,
where is the line between
surveillance and social good?
Or how about, how do we design datasets
that respect people of all backgrounds?
Or, is a hotdog a sandwich?
And studying data science applications
some of you examined
Chicago's traffic data,
And you built an algorithm to predict
the most dangerous intersections.
That's a potentially
life-saving use of data.
It also reminds me of some
of the best advice I've ever received,
and I think it's highly
relevant for you here today.
Look both ways before
you cross the street.
Now this is something your parents likely
taught you years ago.
And yes, looking both
ways is great advice,
especially if you want to avoid being
hit by a self driving vehicle.
But to me, it means so
much more than that.
It means you have a responsibility
to look at a problem from all sides.
And in a diverse world,
one in which we all bring
different backgrounds
and beliefs to a common conversation,
you have a responsibility
to look at a person from all sides.
We know that one of the
best ways to mitigate bias
is to create a sense of belonging.
It's hard to do but it really matters.
And in the board room,
I've been in meetings where
it's painfully obvious
When somebody or some type of
person is talking too much.
In the classroom,
I know you experienced the same,
we won't name names, you know who you are,
but what's less obvious
is which type of person
is talking too little.
Often it's because they don't think
their voices are welcome,
so they stay quiet.
They fade.
And they start to feel invisible.
If you're one of those people
I'm here to tell you
that you are mistaken,
you do belong, we want your input,
we need your voice.
I know it's not always easy
and I know you need help,
so to the rest of us
who could lend a hand,
we have a choice to make.
We can choose to notice,
invite and encourage the quiet ones
to join the conversation.
It's a choice that can yield great impact
and one that I hope all of you will make.
I want to share a secret with you.
The thing that keeps me up at night
is not my responsibility
to deliver billions of dollars
of revenue to Microsoft.
Although I do think about that
about a million times a day.
What plagues me at night
is my responsibility to
create a sense of belonging
for every person in my organization.
My responsibility to create a culture
that can enable change
by fostering inclusion
in the face of inherent inequality.
You see I know our employees
can solve just about any problem,
no matter how hard or how complex,
but only if we empower them.
Only if we see them,
and more than that,
only if they feel seen.
And you've been trained well
to do that here at Berkeley for sure,
not just because of the
diversity of your backgrounds,
but because of the
diversity of your strengths.
Vinitra Swamy is one of our
AI engineers at Microsoft
before she came to Seattle,
she taught Data 8 as at
UC Berkeley grad student.
Vinitra said that when she
sat in a CS class here,
she had looked to her left,
she'd see an economics major,
she'd looked to her right, and
she'd see an English major.
And she noticed how they viewed
the same problem in different ways,
through different eyes,
different experiences,
and different expectations.
That's the start of empathy,
that's the core ingredient of inclusion.
And that's our duty in a diverse world.
The tension of the digital era
is that even as it brings us
together in unprecedented ways,
it's making it harder for
us to connect as humans.
We are tweeting and posting and gramming,
myself included,
but we're not really
talking to each other,
we're not really listening to each other.
We're not really seeing each other.
So before you offer an opinion,
before you come to a conclusion, or heck,
what about before you conclude a meeting.
Do what you do before you
cross the street, and look,
I mean really look both ways.
Okay, second piece of advice,
looking both ways is great life advice,
but when you think about it,
it doesn't tell you the optimal
time to cross the street,
or even the optimal speed.
It simply reminds you to be
aware of your surroundings,
that's why you need to remember this.
Chase awareness, not certainty.
Who's to say that we know
the difference between right and wrong,
I mean I know it's hard to
believe, but these funny hats,
they don't mean we have a
monopoly on knowledge or morality.
Ones and zeros are
unambiguous, they are certain.
The concept of right
and wrong is simply not.
The ethical questions you
have thought about here
are hard ones for sure,
even the hotdog question.
Especially the hotdog question.
But you do have to be aware,
aware that data is intrinsically flawed,
just as the world it describes is flawed,
just as the people who
designed it are flawed.
And they must be aware
that none of these facts
absolve us of the harm caused
if we abuse the power of data.
At my company, we try to
reinforce our awareness
by testing our choices against
a few timeless principles.
We asked things like, is this fair,
is it inclusive,
is it safe and accountable?
Does it respect privacy
and provide security?
Elle Weasel survived the Holocaust
and won a Nobel Peace Prize,
he said that the opposite
of love isn't hate,
it's indifference.
Well indifference is also
the opposite of awareness,
indifference is also
the opposite of action.
Indifference breaks down
community instead of building it,
when we're indifferent
we absolve ourselves
of responsibility when it's needed most.
Recently I was with a
large group of leaders
discussing an important change
we are driving in our business.
And this is a group of
amazingly accomplished
sought after tech executives,
but I've got to tell you,
it was a really tough conversation.
We just weren't getting
the results that we need.
And I was getting super frustrating.
Well eventually we figured it out,
basically everybody in the room
thought somebody else had the ball.
They were counting on somebody else
to rise to the occasion,
effectively they were sitting it out.
You see indifference is
the enemy of progress,
it's the enemy of leadership,
and it's most certainly
the enemy of community.
To make data the best it can be,
we must try to beat its bias,
but you can't beat bias
if you're a bystander.
So how do you keep yourself aware
about what's right and fair and just,
especially after you leave this campus
and the honest conversations it welcomes?
I think one answer is in
the school's leadership principles,
which instruct you to be a student always.
And to that I'd add this,
please be a teacher always too,
because you are the ones who
thought about these questions
more than most people,
you're not just graduates today,
you're not just alumni,
now you're the experts.
Many of you have already
experienced the joys of teaching
by being TAs, mentors,
advisers and tutors.
But to be a teacher
you don't have to stand in
front of the lecture hall
making announcements like Daddy De Niro.
(audience laughs)
You can simply speak up when
you think others are sitting out.
You can make a difference,
especially if you see
others are indifferent.
So please don't just go through
the motions of designing algorithms,
keep your eyes open,
keep yourself open to talking about
the inequities caused by
the worlds inequalities.
And bring that awareness to
your newly earned authority.
Because if you don't, who will?
Okay, I won't be much longer,
pre-came for Game of Thrones is coming,
so here's my third and final piece
of advice for you on your graduation day.
There is only one you,
so ditch this notion of multiple personas.
Back in the nineties when I
was just starting my career,
and come to think of it,
you were all in the womb,
I thought there was a
work Kate and a life Kate,
and I wasn't alone.
Most people in my era were coached
to try to fit into a
standard professional mold,
so I did, I was compliant
and I wanted to fit in.
But then just as you are
all about to find out,
life happened.
And managing two sets of
me became overwhelming,
so I get something rare
and unique for my time,
I brought the real me to work every day,
all me,
every bit of my Kate-ness.
And I ditched the idea of a standard mold.
I started sharing learnings
from my life outside
of work inside of work.
And that's when my career really began.
People were a little
shocked at first for sure,
but they were also delighted and disarmed.
They got insight from me
that they never would
have gotten otherwise.
Here's a quick example.
When I was in my early twenties,
I really wanted my boyfriend
to be able to read my mind so badly,
I mean wouldn't that be awesome?
Well spoiler alert, he failed
to meet my expectation.
So instead I learned how to
share my thoughts with him
more often and more clearly,
and he started doing the same.
And as it turns out
great communication
has been the foundation
for our 30 year marriage.
That experience,
(audience applauds)
there he is.
That experience changed me as a person,
and I brought that new person to work
and help me be more direct and transparent
with my co-workers, my
customers and my partners.
We stopped playing guessing games
and got a lot more efficient
and a lot more satisfied with each other.
Here's a more recent example.
18 months ago I moved to Seattle
away from my family on the east coast.
This means I've had to
delegate the solemn duty
of caring for my aging
parents to my siblings.
It hasn't been easy at all.
You know what I'm learning,
my siblings are amazing.
They are really good communicators,
collaborators and caregivers,
and I love and respect them
more now than I ever have.
Well that experience
changed me as a person too,
so I brought that new
person to work with me.
And I learned just how powerful
it can be to trust teammates
with things I've never
considered delegating before.
My entire team is better
off because of it.
Those are just two ways
that my life perspectives
helped me inside work.
But it wouldn't have happened
if I stayed locked in this notion
That work Kate should somehow
be different from life Kate.
Believe me, all of us on this side
are working hard to build the world
that accepts every single
one of you for who you are.
Don't let us down
by dressing up as a person you think
we want you to be at work.
We want you to be you,
we need you to be you.
I said earlier that our
understanding of how we use data
is still being defined,
starting today right now
it will be defined by you.
Over the last few years
you've mastered the skills of
writing and manipulating code,
now we need you to write the code,
the code of ethics, the code of behavior,
for designing and applying data science.
We need you to be the models
for how one should responsibly
wield this great power
in a world being eaten by software.
It probably won't be as clear cut
as the Constitution or
the Hippocratic oath,
and you might even have trouble
fitting it in a word cloud.
Instead, it will be
written by your actions,
by the example you set
through the choices you make,
and don't forget that every single day
you will make conscious choices.
Will you notice the
quiet person in the room
and invite that person
to join the conversation?
Will you think about how your algorithm
impacts people of all backgrounds?
Will you have the courage
to show up to work
as your authentic self,
and invite others to do the same?
Every day in a million ways big and small
you will make important choices.
Class of 2019,
none of the problems we face today
are really technology problems,
they are human problems.
It's up to you to make sure
the conversations we are having
about the responsible use of data
advance the conversations of inclusion
in real life too.
It's up to you to use the influence
of your incredibly valuable degree
to create cultures that enable change.
That's how we make sure
that in the age of
artificial intelligence,
machines won't be the only ones learning.
Class of 2019, I hope you
remember to look both ways,
to chase awareness not certainty.
And remember, there is only one you.
Congratulations, Go Bears.
(audience cheers and applauds)
- Thank you Kate.
So my name is Michael Jordan
and I don't like Michael
Jordan jokes very much,
but I'm sometimes called the
Michael Jordan of data science
and I do like that one.
Just before we turn all the
attention back to the graduates,
I'll return attention
back to my colleagues John
and Ani for a moment and
go slightly off script.
I graduated in 1980,
and I was doing data science
so I did statistics, computer
science and psychology.
And when I came to Berkeley it was 1998
and I was excited about this blend,
and started to teach it
at the graduate level.
And it was really popular,
and it felt just like a
moment in time had come.
Little by little after
that I went around campus
and tried to find support for
doing this at a broader level.
There was a day about four years ago
where I remember walking
up to Ani Adhikari,
and I'm intimidated by
her, she's so impressive.
And I said I'm thinking
about a freshmen class,
I have no idea how to do that,
and it should be about,
I started to say it,
and 20 seconds later she
turned round to me and says,
"I'm in."
So a little bit later that same day
I happened to cross
paths with John De Niro,
another person I am
extremely impressed by.
Started to give him the same pitch,
and he said after 20
seconds, "Me too, I'm in."
So there was a team that
formed here Berkeley
to do everything that you're seeing here,
I want to also recognize David and Cathryn
who've been core members
of this team from day one
to make all of this happen.
So I felt in that moment perhaps
like the Warriors did
when they got Steph Curry,
that's Ani, brilliant
in every possible way.
And Kevin Durant, the tall
guy at the back there.
(audience laughs)
So I'm now in this moment
thinking we have success,
but what we need is the dream on dream.
So we're in a section
called undergraduate awards,
and every award after that
is gonna be another team
member on the Warriors.
So the first one is
gonna be Dreymont Green,
whose name is Adnan Hemani.
(audience cheers and applauds)
So just to quote the letter
that Ani Adhikari wrote about him,
I think you will see dream out of him.
He's made important contributions
to the grading and
content systems of Data 8,
those aspects of the course aren't fun,
but they are crucial for
its smooth operation.
No one comes close
to Adnan's combination
of academic strength
and dedication to the
instructional program.
So this year citation is
awarded to an Adnan Hemani.
(audience cheers and applauds)
- Thank you Mike.
I'm David Wagner from
Electrical Engineering
and Computer Sciences.
And I have the honor to present
the remaining undergraduate awards.
So we'd like to recognize
the following students
for their dedication
and outstanding service.
I'm speaking on behalf of
the Division of Data Science.
And also the undergraduate
student community.
So as I call out your names
for each of the award winners
I'd like to ask you to please stand
as your name is red,
and stay standing until I've
gone through all of the awards.
And for the rest of you,
please hold your applause,
we'll recognize them at the end
after we've gone through all the awards.
Our first two awards
recognize graduating seniors
who've made a difference in
undergraduate education here
in close partnership with our faculty
through their tireless participation
and integral leadership on
student instructor teams.
So these seniors have
made absolutely essential
contributions to the
education of their peers.
For outstanding student instructor,
teaching category,
I'd like to recognize Sona Jeswani.
(audience cheers and applauds)
Thank you, stay standing so we
all have a chance to see you.
For outstanding student instructor
in the infrastructure
and content category,
Andrew Z. Tan.
(audience applauds)
Our Catalyst Awards recognize seniors
whose dedicated leadership
in the Division of Data Science
student programs and teams
have inspired and energized their peers.
So the 2019 Catalyst Awards go to,
Elanor Fleming.
(audience applauds)
Alexander Ivanoff.
(audience applauds)
Melissa Kim Lee.
(audience applauds)
Subiksha Mani.
(audience applauds)
Great.
We recognize outstanding service
to the UC Berkeley data science community
by graduating seniors
whose personal commitment,
ethical bearing and personal service
has helped set community norms.
So for service to the community,
we recognize,
Sasank Chaganty.
(audience applauds)
Prathyusha Charagondla.
(audience applauds)
Howe Cui.
(audience applauds)
Timlan Wang.
(audience applauds)
William Wang.
Next to last category,
data science is a discipline
that moves from data
to knowledge to action.
And so with our Data
Science in Action Awards,
we are recognizing seniors
whose expertise has enabled them
to make a distinctive contribution
to knowledge and action in the world.
And those three recipients are,
Terry Tae Hyun Kim.
(audience applauds)
Derek Topper.
(audience applauds)
And Monica Wilkinson.
(audience applauds)
And our last award,
one-time only, this year only,
the Early Bird Gets The Worm Award,
goes to our first declared
data science major,
Farbod Nowzad.
(audience cheers and applauds)
Congratulations to all our award winners.
- So, the moment.
Will all the graduates please rise?
(audience applauds)
Please make your way to
the ramp on stage left
so that your names may be called.
And as they go let's give a round
of applause to all our graduates.
(audience cheers and applauds)
As they walk,
along with myself, our
graduates names will be read
by Professor Jim Demil,
chair of the Department
of Electrical Engineering
and Computer Sciences.
And Professor Sandrine DuDois
Chair Designate of the
Department of Statistics.
- You'll count to 35.
- Okay, thank you.
And then does Sandrino
know to count to 35?
Do you want me to count?
- Oh, they're still going.
(people mumbling off microphone)
- Alright and we now commence
with the reading of the names.
Kal Wim.
(audience cheers and applauds)
Andinan Himani.
(audience cheers and applauds)
Catia Williams.
(audience applauds)
Win Suai.
(audience cheers and applauds)
Hau Suai.
(audience cheers and applauds)
Enrique Lopez.
(audience cheers and applauds)
Joanne Chen.
(audience applauds)
Han Song.
(audience cheers and applauds)
Immanuel M. Lukban.
(audience applauds)
Adam Osborne.
(audience applauds)
Nikita Gupta.
(audience applauds)
Sona Jeswani.
(audience cheers and applauds)
Subik Shamani.
(audience applauds)
Akra T. Sing.
(audience cheers and applauds)
Melissa Kim Lee.
(audience cheers and applauds)
(mumbles)
(audience cheers and applauds)
Nemal Emsivpalan.
(audience applauds)
Jessica Whu.
(audience applauds)
Jessica Churney.
(audience cheers and applauds)
Andrew Omed Surati.
(audience applauds)
Eric Javel.
(audience cheers and applauds)
Tedrus Barudwa.
(audience applauds)
Neil Bagat.
(audience applauds)
Anish Saha.
(audience applauds)
Shashak Chagunti.
(audience applauds)
Yosh Sangrajka.
(audience cheers and applauds)
Akshat Das.
(audience cheers and applauds)
Zobeen Bashar.
(audience cheers and applauds)
Derek Topper.
(audience cheers and applauds)
Jevan Mukula.
(audience applauds)
Neha Jen.
(audience applauds)
Tim Lan Wong.
(audience applauds)
Monica Wilkinson.
(audience cheers and applauds)
Bill Foo.
(audience cheers and applauds)
Prathusha Charagandala.
(audience applauds)
Nikhil Krishnan.
(audience cheers and applauds)
Sofia Chang.
(audience applauds)
Andrew Tungal.
(audience applauds)
- [Man] Xaixing Lee.
(audience applauds)
Lui Yun Lin.
(audience applauds)
She Wen Wong.
(audience applauds)
Shel Yu Xian.
(audience applauds)
Jun Yan Tan.
(audience cheers and applauds)
Dorothy Humenglurn.
(audience applauds)
Ja Hung Shacrack.
(audience applauds)
Adonis Ye.
(audience cheers and applauds)
Mulasia Dindradida
(audience applauds)
Shukriadin Omen.
(audience cheers and applauds)
Ryan Wintrong.
(audience applauds)
Yu Ne Choi.
(audience cheers and applauds)
Lewis Jang.
(audience applauds)
Terry Te Hung Kim.
(audience cheers and applauds)
David Young.
(audience cheers and applauds)
Justin Chang.
(audience cheers and applauds)
Jun So Park.
(audience cheers and applauds)
Jemima Shi.
(audience applauds)
June Nam Gung.
(audience cheers and applauds)
Kyle Cho.
(audience applauds)
Hiraki Hishima.
(audience applauds)
Winston O.
(audience cheers and applauds)
Bogun Choi.
(audience cheers and applauds)
Terence Drinkwater.
(audience applauds)
Gilbert Antonius.
(audience applauds)
Henry Hanu Leo.
(audience cheers and applauds)
Daniel Xizen Lin.
(audience cheers and applauds)
William Mitchell Wang.
(audience cheers and applauds)
Se Lam Jacky Wong.
(audience applauds)
Jolina Yao.
(audience applauds)
Dee Xiang.
(audience applauds)
Mark K. Hashimoto.
(audience applauds)
Erica Min Heen Fam.
(audience cheers and applauds)
Eric Ung Yang.
(audience cheers and applauds)
Claire Alana Dubin.
(audience cheers and applauds)
- Thank you.
- [Woman] Garima Rahajiang.
(audience applauds)
She Wang So
(audience applauds)
(mumbles)
Veena Charnomoritz.
(audience applauds)
Crystal Chung.
(audience applauds)
Lovepreed Singh Chaha.
(audience cheers and applauds)
Don Yun Kim.
(audience cheers and applauds)
Ceejay Jang.
- [Man] CJ!
- [Woman] Matthew Brennan.
(audience applauds)
Blake Falon Williams.
(audience cheers and applauds)
Kayla Ray Simmons.
(audience cheers and applauds)
Elanor Fleming.
(audience cheers and applauds)
Vasily Javaris.
(audience applauds)
Alexander Ivanoff.
(audience cheers and applauds)
Hugh J. Shaw.
(audience cheers and applauds)
Farbad Noszad.
(audience cheers and applauds)
Effe Toros.
- [Man] Effe!
- [Woman] Ashley Nicole Purvis.
(audience cheers and applauds)
Ryan Dana.
(audience cheers and applauds)
She Chin Xiang.
(audience applauds)
Se Yun Wang Fu.
(audience cheers and applauds)
Ling Chi.
(audience applauds)
Kevin Morocuin.
(audience applauds)
Kush Cican.
(audience applauds)
Ming Chen.
(audience cheers and applauds)
Blair Gow.
(audience cheers and applauds)
John Siano.
(audience applauds)
Deep Mystery.
(audience cheers and applauds)
- Now we are extremely
honored to have with us today
Chancellor Carol Christ,
the eleventh Chancellor
of the University of California Berkeley.
May I invite Chancellor
Christ to the podium.
(audience applauds)
- Thank you Dean Culler,
for the opportunity to
address today's graduates,
and thanks to you Professor Carson
and Professor Adhikari
for your wonderful leadership
of data science education at Berkeley.
Thank you also to Kate
Johnson from Microsoft
and to UC Berkeley Foundation trustee
Cathy Kwan for joining
us for this ceremony.
To begin, I'd like to echo
the many congratulations
that we have heard today.
Graduates, you are no
doubt experiencing relief,
elation, wonder and apprehension
at the prospect of finishing your degree.
But in addition to all that,
I hope you feel a sense of accomplishment.
You've completed a
demanding course of study
at the Nation's best public University.
And I want to acknowledge the diligence,
the perseverance and the
resilience that this takes.
Beyond that,
I want to congratulate and
thank you for being bold,
for taking a chance on
a new academic program,
for blazing a trail,
and for helping shape
data science at Berkeley
Even as it has shaped you.
This past fall when it
first became possible
to declare an LSN major in data science,
more than 1100 students
signed up in just a few weeks.
Clearly there was a lot of
enthusiasm for the program.
But you were the most excited of all.
You structured your coursework
so that you could be
the first to graduate with a degree,
and many of you also took an active role
in planning out and refining
data science classes,
working with faculty to create modules,
and investing your time and energy
in building up this new
community of scholarship.
For that we owe you a great deal.
You are helping install at Berkeley
an ambitious discipline
spanning new program
that I believe will help redefine
what it means to be a research university
in the digital age.
Back in the 1880s
there was a very famous debate in England
between two scholars.
Matthew Arnold, a poet and educator,
and Thomas Huxley, a biologist.
At issue was whether college curriculums,
which at the time focused
largely on classical literature,
should be expanded to
teach the natural sciences.
Arnold said no,
arguing that the goal of education
was to know the best
which has been thought
and said in the world.
He conceded that an educated person
ought to be familiar
with the important results
of scientific inquiry,
such as Darwin's conclusion
that quote, our ancestor
was a hairy quadruped
furnished with a tail and pointed ears,
probably arboreal in
his habits, end quote.
But how Darwin arrived at
this conclusion Arnold thought
was a concern only to the scientist.
The biologist Thomas
Huxley rejected this view.
Arguing that even for non-scientists
an understanding of science
and scientific methodology
was increasingly necessary
as our society advanced
and humans altered the natural world.
Ultimately Huxley's view won out,
which is one of the reasons you've
had science classes throughout your life,
and likely here at Berkeley.
I bring up the Arnold Huxley debate
because it illustrates the fact
that what we teach our
young people is not static,
and in fact must shift with
the needs of the times.
Today we acknowledge
the growing digitization
of lives and industries,
and recognize that a surge in the volume
and variety and availability of data
means that an ability to navigate
within this data rich world
is rapidly becoming a necessity
for 21st-century citizens no matter
their professional interests
or academic pursuits.
What follows from this is a
need to treat data literacy
as a central competency
for liberal education,
and a recognition that we must weave
computational thinking
and data inference skills
into the fabric of courses
across traditional disciplines.
I've been so impressed
with the model Berkeley has come up with
to address this need.
A model that invests
students with the capacity
to engage critically and
responsibly with data,
that gives them technical
depth that provides a look
at the human and social
implications of data,
and that asks them to
build up a specialization
in disciplines ranging from neuroscience
to sustainable development.
This is an incredibly exciting
new academic direction for Berkeley.
And we owe much of it to those sitting
right here in this room,
both faculty and students.
So thank you for helping
us craft this new division,
new coursework and new major.
Thank you for your foresight,
for leading us down a new path.
For your willingness to help sort out
what is working and what
isn't as we construct
a crucial new academic
offering on our campus.
Graduates thank you,
and please accept my
sincere congratulations
with the coursework you've completed here,
I believe that you,
and those who follow you
will have the skills,
depth of knowledge, creativity,
thoughtfulness and ethical understanding
to make you the leader society needs
in the new data age.
Best of luck, and Go Bears!
(audience cheers and applauds)
Now will the candidates for the degree
of Bachelor of Arts in
Data Science please rise?
(audience cheers and applauds)
By the authority vested in me
by the office of the president,
I grant you the degree Bachelor
of Arts of Data Science
with all the rights and
privileges pertaining thereto.
Graduates, you may now shift your tassel
from the right side of your
mortar board to the left.
On behalf of the faculty
I congratulate all of you.
(audience cheers and applauds)
(upbeat music)
