Notes on Diversity in HPC – Maria Klawe

Once again these are rough notes, unedited, taken during an invited talk at SC16. Some fascinating ideas in here, I hope the notes are useful to you.

Maria Klawe, Harvey Mudd College
Diversity and Inclusion in Supercomputing
Why diversity and inclusion matters:

  • increasing supply to meet demand
  • access to great jobs
  • better solutions to the world’s problems

We can’t meet that demand without enticing more people into these fields. Big data and data analytics are changing every aspect of society today. Healthcare, climate change, clean energy. Big data, supercomputing, machine learning are going to be crucial.
If you want to make a difference to the world, if you want flexibility, travel, these are really great jobs.
When you have a diverse team working on a problem you get better solutions. It can be diversity because of country, race, religion, gender, sexual orientation – greater diversity means better solutions.
Harvey Mudd is a small college focusing on science, engineering and liberal arts. Every student believes it is their responsibility to help everyone else succeed. They emphasise collaboration and communication from day 1.
30-35% of their graduates go on to get a PhD. Second only to Caltech.
In 1996 Faculty and students were around 20% female. 2016 women are close to 40% of faculty, and 50% students.
Racial diversity is also increasing.
It’s not enough to get them in the door. You have to ensure that the experience once they get there is such that they can thrive. That the environment you put them in is one where they can do well.
Females weren’t having as good an experience as the male students, because, for example, all of the speakers were white males. Usually older white males, because the faculty were older white males and inviting their friends.
“They’ll never let me tell them how to run the machine, because they say ‘you’re a girl, you couldn’t possibly know how to do this!'”
These are small things, but when it happens over and over again there is a sense of lack of belonging.
Faculty racial diversity is harder to increase than student diversity, because of low turnover. So we are training our faculty search committees on how to find a diverse pool of applicants, interview them so they have a good experience, etc. But the moment we take our eye off the ball we hire all white males.
Hypothesis: If you make the Supercomputing environment supportive and engaging for all, build confidence and community among underrepresented groups, and demystify the path to success, a highly diverse population will come, thrive, and succeed.
Minorities, like anyone, love the chance to work in an environment where the work you do makes a difference.
Make sure that incoming students feel that they belong, regardless of prior experience. We have students arriving with very different levels of prior experience.
Passionate students with prior experience in CS scare other students off.
So separate incoming students by prior experience. But you will still have some students who just know more. So I meet with the scary students who answer all the questions separately, and I give them the chance to connect separately, so that the other students have a chance to contribute in class.
If you are a female mathematician or computer scientist you go through life with people having low expectations of your technical knowledge. Which makes us more reluctant to ask for help because it seems like we’re not as competent as we should be. This is also true for HM students and many adults at all kinds of levels. So we work hard to set the expectation that asking for help and hard work are far more important than any innate ability.
In our intro classes HM have a black section and a gold section. The gold section is for the kids with no prior experience. The black section is for those who’ve had a year of CS in high school. And there’s a 42 section for the ones who know heaps.
How do you get the gold students to the same level as the kids who’ve already taken a lot of CS. You’ve got to add some extra material to the black course, and it has to be interesting and fascinating, but it has to have nothing to do with the follow on courses, so that they don’t enter the next courses privileged over the gold students.
If you tell someone they’re going to be a great CS student, it doesn’t make it happen if they come from a background where that doesn’t seem likely. You tell them “just take the next course”. And then they start to find that they’re good at it. And you keep on doing that. You make each course engaging and supportive, and make it that they can work hard and be successful. It’s also very important to give early internship and/or research experience. They need to know that what they are working on will make a difference in the world. Then they are more likely to stay in the course.
HM typically takes 40-60 students to the Grace Hopper Women in Computing conference. They take students to conferences with diverse communities to foster that sense of belonging.
They build community – clubs – and ensure access to role models – faculty, speakers, mentors from industry.
Suppose you have a very rigorous, challenging, graduate course. And suppose your students talk about it as “that’s the course where you find out if you really belong in this discipline or not. It’s a really tough course.”
That’s the worst possible way to frame a course. Because you weed out the people with low confidence.
Instead you frame it as rigorous, exciting, and interesting, and you make it clear that if you work hard and ask for help you can do well. Because anyone can do that!
You don’t need to demystify the path to success just for minorities. You need to do it for everyone. To ensure that it’s not just the dominant group who get all the tips and advantages and who therefore succeed.
Best ways to attract diverse graduate students:

  • Recruit diverse undergraduates for summer internships (research and industry) (start early!)
  • Engage diverse faculty in recruiting to PhD programs.
  • Recruit at institutions with diverse students.
  • Include D&I reports on grant applications. Funding agencies to start asking for info on the diversity of the student body and what the institution is doing to increase it. The single easiest way to get institutions to change is to tie funding to progress. So we need to do this for research funding as well.
  • Include diverse speakers at every SC conference. Members of program committees. Videos that you make.
  • (Author aside – on a personal note I would say ban your show floor companies from using women in high heels and tight clothing to interact with the crowds!)

Having a really positive experience early on makes them much more likely to do a PhD.
Numerous studies have shown that we are all biased. There was a study that showed that scientists in Biology with equal faculty gender split were given two sets of resumes. Identical except for first name – Jonathan vs Jennifer. Both male and female faculty rated Jonathan higher than Jennifer, and were willing to pay Jonathan more.
Faculty members at MIT trying to recruit would call other faculty at other institutions to ask for recommendations of PhD students, and both male and female faculty would come up with white males. But if they were then prompted for women and people of colour they would come up with several extra names. It’s not evil. It’s not deliberate. It’s completely unconscious. It’s just part of growing up in a culture where, eg all doctors are male so you tend to think that doctors are male.
So you don’t just post an ad someplace, you actually reach out and ask people, and you ask explicitly for diversity.
It’s illegal in North America to ask about partners, marriage, and plans to have children. You need to make very sure that your recruiters know they can’t ask those questions.
It gets really tiring to be the female who’s arguing for women. If you’re black it gets very tiring to be the black person. But this is the responsibility for everyone, not just the members of underrepresented groups.”


About lindamciver

Australian Freelance Writer, Teacher, & Computer Scientist
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