[APPLAUSE] >> So it's a great pleasure to
be here with you this morning. And I'm going to,
I'm gonna talk about why we need women in data science.
Margo already did it but I will just repeat it.
Then I'm gonna talk about why women should want to
be in data science. Then I'm gonna talk about women at
Harvey Mudd, because we're one of the few science and
engineering institutions where there are women everywhere.
And then I'm gonna talk, I'm gonna talk, finish by telling
you a story of my younger sister who is 63,
who is learning computer science and
data science right now. >> [APPLAUSE]
>> Which I think is pretty
cool. Okay, so, why does data science need women?
I think there are three reasons. One is,
these are amazing career opportunities. They pay well,
they're flexible. I mean why wouldn't we want
women to have those kinds of opportunities as well as men?
Number two, this is, the demand for
people who have knowledge and skills in data science is
growing faster than the demand of pretty much anything else.
And we will not meet that demand in this country or
in any of the countries around the world where there's a wits
of that happening unless we have women there. And
number three which is what Margot said so eloquently.
Most of the major issues that face our world today, data
science is gonna be one of the tools that helps to address
them. And if we don't have a wide range of perspectives and
experiences in the people who are working in those tools,
the decisions that get made will not be nearly as good. So
we need women and people of color and LGBTQ plus people.
We need people from all phase and religions working in this
space. Okay, now, we have some women here. Why
should you want to be in data science? Just amazing career
opportunities Hi, Julia. >> [LAUGH]
>> She's one of my former
students. Number two, I think most women I know actually
want to make a difference in the world. Yeah,
they wanna do something with their lives where they can see
that they're making the world better. And this is just
a tremendous opportunity to do that. But number three is,
and this is one, that's particularly relevant to me.
So I'm 66, and I set out to be a mathematician.
I loved math. I also love art and a lot of other things,
too. But I especially love math. And so,
I got my PhD in mathematics in 1977.
And at that point, about 9% of PhDs went to women in math.
And you know I love to do research, I love to teach so I
was gonna be a math professor. Except that there were
1000 people looking for jobs as math professors, and there
were 83 positions available in all of the US and Canada.
And only probably about a quarter of those 83,
maybe 20 were actually tenure track positions.
The rest of them were visiting positions perhaps two years.
And I was lucky enough to get one of those positions.
My adviser was so proud of me. Well,
then I started that position, it was at a place called
Oakland university that's north of Detroit, and
I hated it. I was teaching, you know, linear algebra and
advanced calculus to students who had difficulty adding
fractions. Was not a good experience.
And I was also single and I was very lonely.
I had one date in the whole eight months I was there. It
was with a jerk. I knew he was a jerk before I took the date.
>> [LAUGH]
>> But that's how lonely I
was. So I was going to a conf,
I was going to the conference every month just as a way
to have some kind of, you know like social experience. And
one of the things I found out was that here I was,
I had solved three 20 year old problems with my thesis.
I thought it was pretty hot stuff.
And I was teaching students who couldn't add fractions.
And I found out that there were people getting their PhDs
in theoretical computer science which did exactly
the same kind of mathematics that I love to do. And
they were getting offers from some Harvard and MIT and. And
I went life is just not fair. And the person I was talking
to said, there's this guy, Andy Yell, he got his PhD in
theoretical physics from Harvard with a Nobel laureate
and then he did his Post Doc at Stanford with another Nobel
laureate. And he realized that his chance of getting
faculty position theoretical physics was basically zero.
So he went and he got a second PhD in computer science from
University of Illinois, Urbana, Champaign.
Now, Andy Yell in 2004 when they're touring the world.
He's currently like he lives in China and he's
currently sort of the Chinese god of computer science,
theoretical computer science. And I went, I could do that,
I could do a second PhD. So one year after I finished my
math PhD, I started at the University of Toronto,
which was then and is still now, one of the top
ten computer science departments in North America.
And they hired me as a faculty member within nine months of
my starting my second PhD. As a regular ten year track
faculty member. So the moral of this story is, if you get
into a field like data science today that is on the rise,
you will have opportunities throughout your career.
Because there will never be enough people of your age. And
I had that tremendous good luck.
I, I moved into theoretical computer science. I still have
stayed in the math community for my career. I married
a young theoretical computer scientist Nick Pippenger,
shortly after I started at the University of Toronto. And
we have worked, we've been able to work at the same
institution for our entire 38 years of being married.
First of all IBM research,
then University of British Columbia,
then Princeton University, and now Harvey Mudd College.
When you go into a rising field, You have opportunities,
unlimited opportunities for the rest of your life. So
I feel incredibly lucky that Andrew Yell did what he did,
he's, by the way his good friend of mine. Because if he
hadn't done that, probably nobody would have told me to
go get a second PhD in Computer Science. And
did I ever get that second PhD? No. [LAUGH]
>> [LAUGH]
>> Did I need a second PhD?
I didn't need a second PhD, I just needed to understand
enough about computer science that I could teach and
do research. Okay, so now I let me talk. So
that's why you should be really thinking, male or
female, about going into data science. And
I was talking to my son yesterday. So my son works for
Square in New York City and he does a mix of data science and
software engineering.
He's a technical lead on their mobile, their information
security for the team for their mobile platform. And
he says, I told him what I was gonna say in this talk,
he saying mommy you're sending a message? He's definitely,
he's a great software engineer, but he loves data
science. And he's definitely headed in that direction.
Okay, so women at Mudd. At Harvey Mudd College,
so first of all, raise your hand if you really don't know
anything about Harvey Mudd College.
It could be worse.
>> [LAUGH] >> Harvey Mudd
College is one of
the five undergraduate Claremont colleges.
Claremont is about 35 miles East of downtown LA.
We are the Science and Engineering college.
We're undergraduate only. We have about 850 students and
a 100 faculty. And, we compete with MIT, Cal Tech, and
Stanford for our students. So they're very strong students.
They know how to add fractions and subtract them.
>> [LAUGH]
>> When I arrived,
I've been there for almost twelve years. When I arrived,
we're about 30% female in the student body, and
about 30% female in the faculty. And in the 50 years
before that Harvey Mudd had been around,
there had been two female department chairs ever.
We have seven departments, okay, we are a small place.
This year five of the department chairs are female.
And every single one of them is the first woman to hold,
be the department chair in that department. Last year,
actually six of the seven department chairs were female.
But one of them became the first female dean of
the faculty, which is our provost equivalent. We,
40% of our faculty now are female. And, you know,
and they're well distributed across all the departments.
50% of the students are female. But what's even
more impressive is if we look at our physics department, and
we look at the students who will graduate this year,
which will be, you know, roughly 23 students or
something like that cuz we're small.
Half of them will be women. Actually, if it is 23,
it will be slightly more than half of them will be women.
In computer science we have three majors,
there is computer science, that's about about 55% women
right now. We have CS math to joint major,
that's about 45% female, the graduating class this year.
And then we have CS math, bio,
which is 80% female in the graduating class this year. So
overall, it's more than 50%. Wasn't that way 12 years ago.
10% of our CS majors were female. And one thing I wanna
make really clear is the fact that we also are typically
between 40% and 55% female in our engineering major.
These numbers are the highest in the country. And the thing
I wanna make clear is that our secret sauce is our faculty.
Definitely not me. We have faculty who care more about
educating our students than about anything
else in their life. And so our, I'll just say, you know,
when I was at Princeton, I taught the second semester of
calculus which was a semester that most engineering students
take. And I would say in our first, our students in
the first half a semester at Mudd, they take the equivalent
to what Princeton does in the first three semesters. So
the curriculum is really intense and very rigorous. But
what we do is we tell all of our students that every
student will succeed if they work hard and get help. And
we provide enormous amounts of support for our students.
And the result is, and we also legitimize that it's really
okay to learn something that's hard for you. In fact,
we all should be learning things that are hard for
us. Because you learn so much more about learning when
you do something that's really hard for you. So
I happen to be particularly uncoordinated. Actually,
my husband is more uncoordinated. So our kids
are even worse than me. >> [LAUGH]
>> But I have learnt to juggle
footballs, I have learnt to ski,
I have learnt to ride a skate board.
I have even learned to compete in a Dancing With
the Stars Competition at the Claremont College,
that was the hardest one. >> [LAUGH]
>> Now, let me talk about
the fact that it's never too old to learn. So
I have a younger sister, as I said. Her name is Katrina. She
is quite seriously, she has a very, she has fibromyalgia. So
she suffers from an enormous amount of pain, she walks with
a walker, and she actually has a PhD in religion anthropology
from Emery University and taught at a university,
in religion anthropology for several years.
And, eventually her, the pain and, just, and fatigue
became too much. And so she couldn't do it anymore. So
three years ago, she started studying computer science
at the Northern Alberta Institute of Technology. Okay,
the other students in her classes are between 18 and
24. And then there's Katrina with her walker.
>> [LAUGH]
>> So she has just created her
own company. And she's working on an app called Fiber Me,
which is an iPhone app so that you can, if when you have
fibromyalgia, that you can record what is happening.
And then she's working on analyzing that data using
machine learning to detect where the patterns are, so
that people with this disease can have a better sense of
what works for them and what makes it worse. And, you know,
she had not done any math since high school.
She never took math when she was in college.
She's getting A pluses. It's pretty spectacular.
I claim that she's the math God for Nate, because,
you know, they don't have any other students
anything like her. She has a mentor now, she has,
actually her name of her company
is Creativity Dot CA spelled with a K cuz Katrina,
her first name is spelt with a K. I am so proud of her. And
so the thing that I wanna communicate to all of you
is, It doesn't really matter whether you think you're good
at math or good at computer science. That's not relevant.
If you want to make an impact in this world,
learning something that is challenging for
you, learning the tools, the basic concepts,
and developing the skills to use the tools of data science.
You can do it. And you're gonna hear from some
amazing women today who have been doing exactly this. And,
The single most important thing to me that you take away
from this conference, and I wanna thank Margo and
Karen and Judy, and all the others who made that possible.
The single most important thing is, you need to be able
to encourage not only yourself to take the risks and
put yourself out there to learn new things, but
everyone around you. And you know really,
the secret sauce that Mudd is, we work hard, we encourage,
we support, we care about each other. So thank you very much.
Have an absolutely fantastic day here.
Be educated, be inspired. And be ready to go out there and
change the world. Thank you. >> [APPLAUSE]
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