(light music)
- Welcome to the Winter Commencement
for the Master of Information and Data Science Program
at the UC Berkeley School of Information.
I want to first welcome the 68 graduates
who are here today in person.
We are here to celebrate
their completion of the MIDS degree.
This is a really impressive accomplishment,
not only because they've completed a very vigorous
and demanding curriculum,
but also because a majority of these graduates have done it
while working part time or full time.
Today, we're also celebrating the 64 classmates
who also are graduating at the same time
in the summer or fall of 2018
but aren't able to join us today.
I also want to welcome the I school faculty and the staff
who are here today to celebrate this accomplishment.
They provide the substance and the support structure
for this educational experience.
We couldn't do it without them.
And last but not least, I want to welcome the families
and friends of the graduates
who have come here from all over the country,
indeed all over the world
to celebrate these graduates today.
I want to especially thank those of you who are parents,
children, spouses and friends of the graduates
for supporting them throughout this degree program.
As some of you know, the MIDS degree is new
or relatively new on the Berkeley scale, it's very new.
It was launched in 2014.
Today we're celebrating the largest
graduating class to date.
So the fall cohort is 75 graduates.
This means that including this cohort to date
we have 512 MIDS alumni.
That's a lot of alums.
They are located all over the world.
They are becoming, so you graduates
are becoming part of a very dynamic
and fast growing a network of MIDS alumni
who are and will continue to be a source of career advice,
support, friendship, and hopefully lifelong colleagues.
I just also want to mention that we have some people here
who've come from a long way.
We have graduates here, one from Columbia,
two from Zurich Switzerland,
one from London.
Have I missed anybody who came from really far away?
Where'd you come from?
Where?
Canada, that other country.
(laughs)
Toronto.
We also have guests in the audience
who are coming today from Spain, Seattle,
San Diego and other parts of the US.
I want to share just a few words
about this particular class of graduates.
They're currently working
in a really wide range of industries and companies.
The tech industry is as always, one of the most popular.
We have graduates working at Amazon, Apple,
Facebook, Google, Microsoft as well as Pinterest,
Uber, Bird and Verizon.
Several graduates interestingly, are also working
in the aerospace and aviation business, at Boeing,
Northrop Grumman and American Airlines.
others can be found working in Pharma and Biotech
and companies like Genentech, Novo Nordisk and 23 and Me
and still others are working in consulting at Slalom,
Oliver Wyman, and LEK Consulting.
I think this gives you a sense that data science
and data scientists are now
everywhere throughout our economy
and we are part of educating this new cadre of people
who will be working with the data and helping gain insights
into all of the industries, both old and new.
Two of our graduates today have earned the Jack Larson
Data for Good fellowship.
And I just want to call them out.
It's Vicky Foss, graduated 2018 spring
and Divya Shreerum from May 2018.
(applause)
The Jack Larson Fellowship is designed
to support MIDS students in the pursuit of a career
that will allow them to use data science
to improve human life and benefit society as a whole.
We're very happy to see the two of them
complete their MIDS degree today
and continue the important work in this area.
I also want to acknowledge the hard work and commitment
that many of these students have put forth
throughout the past several years.
We know that juggling work and school is not easy.
It's very hard.
At least two of today's graduates
have added a new baby during their time in the program.
I don't know if that's one of the voices
we're hearing right now.
Many of them travel a lot for work,
making it even harder to stay in school.
One graduate acknowledges
that he's dialed into live sessions
from five different countries
throughout his tenure in the program
and that includes the US, China, Canada, Italy, and France.
Now that's real dedication.
Others have pointed out how their studies
have inspired their own family members as well.
We have one graduate today whose wife
ended up deciding to apply to and was just admitted
to Master of Public Affairs
at the Goldman School of Public Policy
right here on the Berkeley campus.
So congratulations for that.
(applause)
Now I'd like to turn the table
over to our MIDS commencement speaker, Amit Bhattacharyya.
Say it for me.
Bhattacharyya, I'm sorry.
Amit is head of data science at Vox Media,
a modern media company focused on connecting
with passionate and curious audiences.
Amit works across all of Vox's web properties
in developing models to better understand audiences
as well as working to deliver innovative product solutions
such as personalization and recommendations to the platform.
Previously, he's led data science initiatives
at both in education technology startup
and an advertising agency.
He started his career working as quantitative analyst
at various banks and hedge funds
in New York City for 12 years.
Amit has been teaching in this program,
the MIDS program since 2016.
He's enjoyed teaching courses as diverse
as the introductory python class,
data storage and most recently, machine learning.
He particularly enjoys bringing real world examples
from his work to the classroom.
Amit earned a PhD in physics from Indiana University
and he is a UC Berkeley alumnus
having attended Berkeley and major in astrophysics
and classics as well as astronomy.
And he is a proud and long suffering Cal football fan.
Go Bears.
Teaching has always been a passion of his
and having a strong connection to his alma mater
has been a dream come true.
In his spare time, Amit rides his road bike year round,
he goes skiing at the first sign of powder
and he dabbles with machine learning algorithms
for fantasy football.
He lives in New Rochelle, New York
with his wife and two teenage daughters,
both of whom have been known to occasionally show up
for Amit's class or two.
With that introduction, I'm going to turn it over to Amit.
(applause)
- All right, well thanks Anna for the introduction
and congratulations and welcome to everyone who's here.
I heard that last year,
at the graduation commencement ceremony
that the speaker sang a song for you guys.
It was like climb every mountain
from like the sound of music.
So I thought I would start by singing
a stairway to heaven for you guys.
All right, I'm kidding.
I'm not going to sing.
You don't want to hear me sing.
But it's really an honor to be here.
It's nice to see you guys in person, not in a Zoom meeting.
I was super excited that I got invited to speak to you guys.
I was hoping it wouldn't be in a Zoom meeting
for commencement, so it's great to be here.
So yeah, we should just definitely start
by welcoming all the graduates and the families
and the supporters.
You guys all have done a tremendous amount of hard work.
So I would like honestly give yourselves
a round of applause, everybody.
(applause)
We should also acknowledge this is a ton of hard work.
Like Anna said over the last hour long
it's taken you to do this program,
put in a ton of hard work
and you've probably also been supported by your friends,
your family members as well as your kids.
And it's been great.
Sometimes I'll be teaching class
and people will be dialing in from remote locations.
Sometimes somebody is on an exotic vacation,
you see palm trees waving in the background
but then plenty of times there's like some crying babies
in the classroom as well and it's really nice
cause you can see that all of us are incorporating
MIDS into our life in a very different
and innovative way than teaching used to be,
20 years ago or whatever.
And in fact like Anna said,
my kids occasionally pop into the classroom.
They'll come and ask me what's for dinner
or they'll just want to see who is online.
They just want to see what it's all about
and they are just fascinated
with the way that this can be so effective.
And I'm also amazed.
When I started teaching, I was just like,
I don't know if this is going to work,
but in fact it does work and I love teaching in this program
using the technology that we have.
What I had in mind to talk to you guys today about
is your data science journey.
You're already well along your way
on the data science journey
and then also for the supporters here what that means.
I'm sure everybody has some sense of what data science
and what you're doing but maybe,
we talk a little bit about it.
So we start with what is data science?
I've been working and teaching
with data science for a while.
I know what it is, but it's not a short explanation.
Have you ever tried explaining data science to a friend?
Does it take about an hour?
So and at the end, after the hour,
what does your friend want to know?
So what do you think of self driving cars?
Well, what do you think of the latest Facebook scandal
and what did you have to do with it?
Or isn't online targeted advertising terrible?
I just bought a pair of shoes.
I don't need to buy those shoes again.
I don't need to see that in my advertising feed.
I work in advertising agencies before,
so maybe I'm partly responsible for all of that.
But all these things aside,
for the benefit of the people who are here
supporting you today, we'll talk a little bit
about for the next hour about all the things
that you learned in this program.
I'm just kidding.
I'm not going to talk for an hour.
Just like 10, 15 minutes.
So let's touch some of the parts of the program
that you might've been valuable to you.
I haven't taught all the courses.
I've taught a few and I've enjoyed all the different ones.
So feel free to cheer loudly if any of these
were your favorite part of the MIDS program.
We'll talk about exploratory data analysis.
We'll talk about feature engineering,
we'll talk about machine learning
and we'll talk about ethics and values.
All right, that's good to hear
because that's the longest part of the thing.
So the people in the MIDS program,
of course, have done a ton of exploratory data analysis.
Usually it's the thing that you do
before you start modeling, before you go and launch
onto a big complex form of analysis or a model,
you want to get a sense of what does your data look like?
What are you dealing with?
Let's take for example, your choice
to enroll in the MIDS program.
You probably had to figure out
what are all the programs out there?
How much do they cost?
How long is this going to take?
How many hours per week should I be expected
to work on this program?
Maybe you put it all into a spreadsheet,
maybe that's exploratory data analysis.
Doing your kind of like basic work
and then you thought about it very hard.
Maybe you created a model,
I don't know what you did to decide,
but you made a good decision.
You came to the Berkeley MIDS program.
So moving on from exploratory data analysis,
we could talk about feature engineering.
What does feature engineering?
It's just kind of a fancy word, right?
Feature engineering is just deciding
which variables you want to use in your model,
whether it's a fancy model or not.
So let's say we decided to construct a model.
We're here in Berkeley today and we're deciding
whether we should bring a rain jacket with us or not.
What are the variables like your model might include?
You could look at the forecast.
You could look at what time of year it is.
You could actually look outside
to see how if it's raining or foggy
and if maybe you look to know
if there's like an epic drought in progress.
Luckily for us, this model is really simple.
You always take a rain jacket.
No matter what the variables are or what your model says.
Let's turn to machine learning.
You might guess this is my favorite part and in fact, it is.
I was drawn initially to data science
because of machine learning
and some of the mathematical underpinnings
of all the algorithms and stuff.
I'm sure everyone has a different story
of why they think machine learning is cool.
But I've been using these kinds of algorithms
professionally for a while and then I've come to realize
that machine learning is like only as good
as the data you choose to use
and the mindset that you enter into
when you're going to start using machine learning.
It's still a relatively morphous scientific process
to decide what question you're going to ask of your data.
Even if your boss comes to you and says
hey, do this or do that,
it's still not always clear
exactly what your variable should be.
What is the thing that you're even trying to predict?
And if any of that stuff will actually fit into your model.
It's not like you just take all the data that you have
and throw it into this magical black box
and amazing results come out all the time.
In fact, that never happens even once.
You can never just even by accident,
you can't throw into your data into a black box
and nothing ever will come out.
So one of the things that that happens to me,
especially in teaching machine learning,
the most common question that I get over the semesters
and even over the course of the class
is what's the best model to use?
I don't know.
But then the secondary question is well,
what process should I use to figure out
what the best model is?
And even to that,
there's like not necessarily a right answer.
There's lots of different models.
Some are good for certain things, some are good for others
and I think you guys have hopefully learned that
over the course of the program.
But the one thing that I will point out to you guys
is that let's say you know where you're working at a company
and they're not using any machine learning models
and then you show up and you do something clever and cool
and it shows some improvement of some sort.
No one's ever gonna say, that's bad.
Everyone's gonna be happy.
You're like a genius.
And then let's say you take another model
that's already in production and you make it better.
That's also even better.
No one will ever come to you and say
that a better model is not what we want.
And especially if you're improving efficiency
or there's cost savings
or outright like profits for your company.
Very few people will tell you that's not a good thing.
The thing is, and this is what how I answer
what's a good model in machine learning
is that's not even like necessarily the right question.
The way I answer it is that, it's up to you
to question everything about your model.
Why is it going to break?
When is it going to break?
What assumptions are you making?
What happens if those assumptions aren't true?
I used to work in the financial industry
leading up to the credit crisis
and we had a lot of financial mathematical models.
They were based on a lot of assumptions
and when those assumptions weren't true anymore,
all sorts of bad things started to happen.
So I was still will tell all of you guys
that no one is ever,
it's really hard to know what's wrong with your model
unless you think to question it.
People are rarely going to come up and tell you
to look for racial bias in your models.
You won't even know to do that
unless you think to do that yourself.
And basically, you want to do this stuff all in advance
and yes, it slows work down and yes, it takes more time
but ideally, you want to find out
what's wrong with your model
before it's on the front page of the New York Times.
So as machine learning improves and all that kind of stuff,
we're probably going to have all sorts of smart robots
entering in our life.
Right now you guys are probably subject
to some not so smart robots in your life.
Their names or Alexa and Siri
and then there's a third one who's name is Okay Google.
But whenever I think of these robots,
I actually think of some short stories by Isaac Asimov.
They're called the I robot stories.
Has anyone ever read this stuff?
All right, excellent.
All right, so in these stories, which were written
back like in 1940s, 1950s,
well before smart robots actually existed,
there were these three rules that he had.
They were called the three laws of robotics
and I'm going to like read them to you
so I don't mess them up.
So law number one, a robot may not injure a human being
or through in action allow a human being come to harm.
That seems pretty reasonable.
You don't want the robots killing us
like they do in the Matrix.
Law number two.
A robot must obey orders given it by human beings
except where such orders would conflict with the first law.
So that also seems pretty good.
You don't want like an evil tyrant
taking over the robots and harming everybody.
And then the third law
is a robot must protect its own existence
as long as such protection does not conflict
with the first or second law.
This one seems like the most humanizing
because it gives this robot a sense of purpose,
a sense of existence, a sense of self.
So these laws, they seem all very reasonable
and a set of like rational set of rules
for a robot to live by.
In fact, if I read them again,
they might not be terrible rules
for humans to live by either,
but that's an entirely different matter.
But we can imagine, but you know what happens
in these I robot stories
is basically that the robots
are in all these different situations
and they proceed to go completely bananas.
Then they do all sorts of unexpected things
and even though they're following these three basic rules
and it's programmed into the robots,
all sorts of crazy stuff happens.
And that's the interesting part of the stories
is that it shows you that even if you have written down
a set of rules to live by or analyze something mechanical
or automated like a robot, things can still go wrong.
And that's probably true of machine learning as well,
which is that, we could set ourselves some rules
and we know how our machine algorithms work
and we have essentially of what the assumptions or not.
In the end, you guys are the data scientists.
You will have to use your judgment
and you will have to do more than just follow
a set of the three laws of machine learning or robotics
or something like that blindly.
So where does that leave us?
That basically leaves us back to questioning everything.
So please, please, please as you develop
your machine learning models and you're doing this stuff
out in the world and the universe, question everything.
And honestly you will like your job better for it I think.
All right, so the last part I'm going to talk about
is ethics and values.
And it's great that everyone cheered loudly
for that part of the course.
I don't teach that course, but I've heard pretty much
more or less universally,
that's one of the strains of the MIDS program.
And I'm really happy to hear that.
Basically we're trained to think carefully about our actions
and we can call it ethics or values
and I think that the fact that you guys treat it
as one of the nice courses or one of the better courses
that rounds out your education in a really amazing way,
that speaks to how important it is.
So I don't want to stand here and tell you do you know evil
or you shouldn't work in certain industries or whatever.
But I just thought I would tell like a little story
and it's a little story that I made up
but it might tell you your a feature work in perspective.
So the story is called
The Parable of the Baby and the Puppy.
All right, so the way it goes,
there's three characters in this parable.
There's a self driving car and there's a baby
and there's a puppy.
But right now let's leave the self driving car out of it.
Let's say I'm driving down the street
and I encounter a crazy situation
and it's like I'm going to get into an accident.
I slam on the brakes
and I'm either going to run over the puppy or the baby.
And in both cases it's bad.
And what do I choose?
The point is, humans don't really have
the reaction time to choose.
Something happens and it's an accident
and you're not really held liable for your decision
to either run over the baby or the puppy.
Now fast forward to when self driving cars
are driving around.
It's very possible that a self driving car
has the reaction time to be able to decide.
And then if we think about machine learning
and how it works, usually machine learning works
by giving the computer a bunch of training examples
and the computer learns right and wrong
from what it should do and what it shouldn't do.
That's kind of like the basics of supervised learning.
I bet you there's not very many examples
where the computer has to decide
between what two items to run over.
And so you know what I could imagine,
and this is just me speculating into the future,
that the thing is given that the car
has enough reaction time to decide
whether it's going to run over the baby or the puppy,
somebody like a data scientist
will actually have to code into the algorithm
which one it should choose.
Someone might pick the baby.
Someone might pick the puppy
and there's probably no right or wrong answer
except when something bad happens,
someone will be upset at the choice, no matter what.
So look, this isn't meant to be like a depressing story,
but it's meant to be a reminder that there's a lot more
to data science than just data science.
And hopefully, this is exactly what you experienced
in this program.
Hopefully you learned that you learned a lot of techniques,
you learned a lot about Python,
you learned a lot about statistics, database
and all that kind of stuff.
But in the end, as a data scientist,
you're a human that's going to be out there
and making decisions and hopefully,
this program will culminate
in you guys making data science decisions
in your professional career.
So again, good luck to you guys.
Thank you for all your effort and hard work
because that's honestly what keeps us
as the faculty coming back
and enjoying teaching in this program and Go Bears.
(applause)
- Now you can see why Amit
is one of the most popular teachers in the program.
Thank you so much.
Now I'm really delighted to introduce
the MIDS student speaker.
Alberto Megoza Paez.
Alberto.
(applause)
- I don't know how long I need to be holding on my name tag.
I just brought it with me just in case.
Hi everyone.
Well first of all, congratulations.
(applause)
This is a remarkable accomplishment.
It sort of feels like at the end of every term.
It was just so hard in the end
but also so rewarding and it definitely takes a village.
So it is important not to just recognize our own efforts,
but the efforts of all of those around us.
I personally know that there's no way that I would have been
able to pull this off if it weren't for the efforts
and encouragement and support of my incredibly patient wife,
my dearest friends, my family,
my fellow students, our faculty members
and everyone at the School of Information
that worked so hard to help us be successful.
So to all of you, thank you.
(applause)
I also want to take this moment to give a special thanks
on behalf of all the MIDS student community
to our dearest dean.
(applause)
Her leadership and relentless work over the past five years
are what made this program what it is today.
She was instrumental in getting
this new online program approved
which was one of the first ones at UC Berkeley at the time.
And now it is a program attended by students
literally around the world.
So thank you dean for everything you've done
to data science around the world.
(applause)
I'm really excited and thankful to be here.
Oh and by the way, this is an online program.
So Catherine just told me
you can actually attend a ceremony online.
You just need to go to ISBC
and you'll see the button there for the meeting.
If the button doesn't work, let us know
and we'll post the link on the Slack channel.
As I was thinking about what my message for today
ought to be and I was reflecting
on my experience in the program,
one thing came to mind that I felt compelled to share today
and that is something that I learned about data science
that I didn't expect.
When I first enrolled in the program,
I understood data science to be in essence,
a technical discipline.
After all, it is science and it is data.
And as I went through the program,
of course we learned the mathematical
statistical foundations that machine learning
is built on top of, for example.
Of course we learned about Python and tensor flow
and Pandis and Spark and all the tools
that data scientists use on a regular basis.
But I was surprised to realize how data science
is actually a human-centric discipline
and we just need to look at the amazing capstone projects
that we have this term to see how this is true.
We were able to see how data science
can improve people's quality of life,
like helping folks with hearing disabilities
become aware of their nearby sounds,
thanks to their mobile devices, recognizing those sounds
and sending notifications.
We were able to see how data science
can help small business owners mitigate online reviews
that can unfairly hurt their businesses.
We were able to see how data science can make it easier
on busy parents cook meals for their children
by suggesting recipes that they can make
with ingredients they have already
in their pantries and their fridges.
We were able to see how data science can help two humans
that speak completely different languages
understand each other in real time.
So there is a lot of talk out there
on how AI is going to replace a lot of people
in a lot of what we do and how if it left unchecked,
we're going to end up with the terminator
and while there is certainly very important work
that needs to happen in the field
of ethical AI research and development,
work that many of us data scientists
will probably be part of.
What I saw more clearly during the program
is actually the opposite.
It was data science being a force for good.
All these examples that I mentioned,
making apparent how data science
is in fact something that allows us
to be closer together as human beings.
It gives us tools that help us
understand each other much better,
no matter how different we may be.
There is no doubt the data science and data scientists
are going to continue to transform
every industry in every corner of the world.
And I can wait to see
how we can transform the world together.
Thank you.
(applause)
- Thank you so much Alberto.
I can't tell you how happy I am to hear
that you took that lesson away from our program.
That's one of the things that we care the most about,
that people understand that it is not just
about tools and techniques, but also we're about people
and people working with other people
and people working with information
to help improve the world.
So thank you.
Now, I am delighted to present
the MIDS faculty awards.
That's the first thing.
So we're graduating two different cohorts now.
Summer 2018 and then fall 2018.
So for summer 2018, the award goes to, Kevin Crook.
The instructor for W 205
which is the Fundamentals of Data Engineering.
Unfortunately, Kevin wasn't able to be here,
but he's been teaching today,
but he's been teaching in MIDS since spring 2017.
And I want to just thank all the students who voted for him.
(applause)
For fall 2018, I want to extend the award to Alex Hughes.
(applause)
Alex teaches W 241,
which is Experiments and Causal Inference.
He has been, a faculty award recipient in 2016 and 2017.
We might have to stop this.
Not allow you to vote for him anymore.
And we're delighted that he's also recently
taken the role of head graduate advisor
for the MIDS Program.
So congratulations. - Thanks.
(applause)
- I'm also delighted for the first time,
I'm delighted to announce the best TA, for the MIDS Program
who is Robert Foster.
Robert.
(applause)
Robert has been the TA for W 200,
which is our Python class.
For the past four terms, he started in spring 2018
and he consistently receives very high marks
on course evaluations for providing course support
and timely feedback.
So congratulations.
(applause)
Now I'm really pleased to announce
the Howavarian MIDS Capstone awards.
These are for those projects that Alberto just mentioned.
Every year we give one, usually one for each semester
for the best and it's always difficult.
It's always difficult to pick the best capstone
because so many of them are so good.
Howavarian by the way was the first dean
of the School of information
is now the chief economist at Google
and he generously donated the funds to support these awards.
For summer 2018, the award goes to Elect Bot AI,
Benjamin Attics, Christa Mar and Projecta Pankhakar.
Is anybody here?
(applause)
I want to say one word.
So Elect Bot AI is a chat bot that helps inform voters
on important issues and candidates stances
as well as remind voters about upcoming elections.
Elect Bot also had recent partnership with vote.org
over the midterm elections past November.
So congratulations to you.
(applause)
And for the fall 2018 semester,
the award goes to Parasite ID.
(applause)
This includes Cameron Bell, Vicki Foss,
Kiersten Henderson and Nathaniel Chubb.
Parasite ID is a tool designed to facilitate
diagnosis of parasitic diseases
using low cost cell phone microscopy
and automated image analysis.
Congratulations to you.
(applause)
One more hand for Parasite ID.
(applause)
And hopefully we will see these two projects
continue to evolve in the world in the future,
even though they're going on
to do other things and leaving the MIDS program.
Now, the moment that you've all been waiting for,
the conferring of the degrees.
Before we start, I just want to remind the students
when you come up, when your name is read,
to pause when you shake my hand
so that the photographer can actually take your picture.
We have people who come up and are very anxious
and move through too fast.
So, this is the moment we've all been waiting for.
I just want to thank again all of the parents and friends
and families and siblings and spouses.
This is an amazing accomplishment.
I really am in awe of these students.
I don't know that I could have done this
to actually work full time and go through a program
that is demanding as this.
I have tremendous admiration for you
and I really wish you all the very best,
as you move forward in your careers.
Drew Paul, the academic director for the MIDS program
will come up and read the names of the MIDS graduates.
(applause)
(laughs)
- Omar Altahir.
(applause)
Michael Alexander.
(applause)
Ben Attics.
(applause)
Ramia Bella Supermanium.
(applause)
Mark Burnett.
(applause)
Chris J. Beecroft.
(applause)
Cameron Oliver Bell.
(applause)
James Black.
(applause)
Andress Barrero.
(applause)
Robert Yu Dang.
(applause)
Vaibhav Dijuan.
(applause)
Jumo Fan.
(applause)
Heather Feinstein.
(applause)
Vicky H. Foss.
(applause)
Robert Foster.
(applause)
Amad Gangurdy.
(applause)
Ravnit Gumar.
(applause)
Saul Grimaldo.
(applause)
Chad W. Harness.
(applause)
Sean He.
(applause)
Kiersten Henderson.
(applause)
Praddepta Ario Ascoro Hendry.
(applause)
Alexander J. Herring.
(applause)
Cal Hayne.
(applause)
Shelly Sue.
(applause)
David P. Huber.
(applause)
Jason N. Hunsberger.
(applause)
David Gibblonsky.
(applause)
John R. Kenny Jr.
(applause)
Boris Clickser.
(applause)
Alex Lau.
(applause)
Joshua J. Lee.
(applause)
Chen Lin Liu.
(applause)
Jensen Louie.
(applause)
Andrew David Mamroth.
(applause)
Krista M. Mar.
(applause)
Alberto Megoza Paez.
(applause)
Vincenzo Moshia.
(applause)
Nishon Tucnair.
(applause)
Surya Nimigarda.
(applause)
Saad Padella.
(applause)
Projecta Pendhakar.
(applause)
Sukait Tulsidas Patel.
(applause)
Jayashree Ramhan.
(applause)
Noah Von Randolph.
(applause)
Jason A. Rosenfeld.
(applause)
Maneesh Sunat.
(applause)
Nathaniel J. Shoob.
(applause)
Maneesh Shah.
(applause)
Elizabeth A. Shoelock.
(applause)
Jonah Smith.
(applause)
Divya Shreerum.
(applause)
Simon D. Storey.
(applause)
John V. Tobone.
(applause)
Stanamir M. Vichef.
(applause)
Kim Fignula.
(applause)
Jessica Vincent.
(applause)
Daniel Wald.
(applause)
Shao Wu.
(applause)
Harry Shu.
(applause)
Steve Yang.
(applause)
Yi Sang Yu.
(applause)
Todd Young.
(applause)
Congratulations to all of our MIDS graduates.
(applause)
- I had some notes.
These are the wrong ones.
Congratulations to all of you.
I hope that you do well, have fun
and please stay in touch with all of us
and with your classmates.
I couldn't be prouder of every single one of you.
I also want to before we go out and celebrate
at our reception, I want to especially thank
the staff of the School of Information
for all of the hard work and for doing such a wonderful job
setting up the graduation.
(applause)
And now, please join me for a drink outside.
(applause)
(jungle drum beats)
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