It was in 2015 when Margot Gerritsen was asked to speak at a data conference with not a single other woman on the program that she knew that something had to be done to get women into the field.
As then-director of the Institute for Computational and Mathematical Engineering (ICME), Gerritsen knew more than a thing or two about data science and became determined to change the male-dominated culture.
This determination led to the creation of the wildly popular “Women in Data Science Conference.” In putting the first agenda together, she was insistent that the conference be not about the problematic state of women in the field, but on the exceptional science of the attendees.
Now into its fifth iteration, with more than 100,000 participants worldwide, online and at satellite events spreading into six continents, Gerritsen and her co-directors of the conference have inspired women across the planet to enter the sciences and provided a platform for them to highlight their work. In addition to the conference, WiDS now includes a datathon, a podcast that Gerritsen hosts, and ongoing education programs. The results have been, quite literally, life changing for many.
Russ Altman: Today on The Future of Everything, The Future of Women in Data Science. Despite some progress over the last decade women are still very much underrepresented in fields like data science, computer science, artificial intelligence. The reasons for this are complex, they include cultural, social, historical and other influences. But as a result there’s been a number of grassroots efforts to attract and retain women in these fields. These include organizations like Girls Who Code, that encourage young women and girls to learn computer programming and get interested and excited about these fields. But the statistics in these measures at many schools, not all, are still greatly skewed with many more men. This is just not an issue of fairness and balance, there are published studies that show that diverse teams come up with better solutions they generate different perspectives on problems, and they often design more robust approaches to those problems.
Professor Margot Gerritsen is a Professor of Energy Resources Engineering, she is a Senior Associate Dean for Educational Affairs, and a senior fellow at the Precourt Institute for Energy. She is devoted to work in computational analysis of fluid dynamics, but has led many initiatives in education and research to ensure that women are appropriately attracted to data science and find good career opportunities in it. Margo, how did this challenge of women in data science come to your attention, and how big of a problem is it for society, both industry and academics?
Margot Gerritsen: Do you want the blunt version or the light version?
Russ Altman: I would like to blunt version here on Future of Everything.
Margot Gerritsen: I’ll tell you. In 2015, early 2015, I was talking to one of your colleagues, and they were organizing a conference in data science on campus, and they asked me if I could speak. And at the time I was leading the Institute for Computational Mathematical Engineering, and I looked at educational programs through data science, and I thought it would be interesting to talk at this conference, but I couldn’t make it. And a week later I met them again, and I said, “Well I saw your program online, and all the speakers are male.” And one of them turns to me and said, “But Margot, you couldn’t make it.”
And I was just astounded to think, wow, it’s 2015 and we’re still in a situation where we have without even thinking about it all that much, without thinking it strange or unacceptable, we have these situations where we have all males, and obviously not enough awareness of other women also in this field. So I said to the organizers, there’s so many women though in data science so you can just pick somebody else. They said, “We looked and looked and looked and we can’t find any.”
Russ Altman: Oh my goodness.
Margot Gerritsen: No, it still happens. And of course I’m from a field where I’ve always been one of the few women, or the only woman, or the first woman in my department. Or the first woman in ICME, it’s always been part of my life. And I thought to myself wow, I’m almost 50 and it still this case, what can I do about this? And so my first reaction was actually I’m gonna have a revenge conference.
Russ Altman: A revenge conference?
Margot Gerritsen: A revenge conference, I’m gonna show them that it is possible to get outstanding women talking about outstanding work, really focusing on the technical work. And I was having lunch with my very dear colleague and co-director Karen Mathias, and an ex-student of mine, a former student of mine, Esteban Arcut, who then worked at Facebook. And we all sort of decided right then on that lunch at Coupa Cafe on a Wednesday that we would just put a conference on women in data science. And they said, “When do you want to do this?” This was May, I said, “November. Why not, six months and we’ll see.” And it was a great success. But it really started as sort of a revenge conference, yeah.
Russ Altman: Now you said something that I noticed a moment ago, you said you wanted it to be a technical conference. So it sounds like you weren’t so much interested, even though it’s important to talk about the social issues and cultural issues about getting women involved in data science. For this first conference you wanted them to talk about their work, their technical work in the field. Is that true or am I reading too much into it?
Margot Gerritsen: No this is absolutely true, and as a woman in a very strongly male dominated environment, and I’ve been in an environment like that for 35 years or so.
One of my frustrations has always been, is that women are invited to be part of the conversation, but then they are often forced to talk about how hard it is as a woman in this field. Or what they can do for other women to enter the field. And I always thought if we just talk about the technical work that we do and show that we can perform really well and do outstanding work just like any of the men. These questions may now actually be irrelevant.
One of the reasons I think why a lot of women don’t enter the field and where a lot of men still have bias against women, is because they simply do not see enough very strong technical work done by women promoted.
So we said no, this is not gonna be a conference about how tough is our life as women in data science, or a conference about we want to inspire the young girls to come in. We are doing that, but we’re doing that through showcasing this really fantastic work done by all these women. And there are many, many also here in Silicon Valley. And right now after our four conferences, we’ve done four conferences now and we’ve gone global, so —
Russ Altman: Yes, so I wanted to say that, that first revenge conference was not just a one off. You actually created an organization. So tell me, what was the path from, forgive me, I love this, from a revenge conference to this really useful global group of people, so how did that all happen?
Margot Gerritsen: A revenge conference can be kind of useful too.
Russ Altman: Yes.
Margot Gerritsen: But it is quite useful. So what happens is that in November 2015 we put this conference on and we start at live streaming, because we thought that would be nice, and Stanford helped out, we were holding it here on campus. And to our surprise without really promoting it all that much, we got maybe 6,000 people really joining us on the live stream.
Russ Altman: That’s a significant, yes.
Margot Gerritsen: And that was a big thing. And I said to Karen and then to Judy, my other co-director, “Wow, we really hit a nerve.” And then we thought we should scale it up. We also sold out tickets to this conference in just a few weeks which was what quite something. We put the tickets up for sale I think it was in early September.
Russ Altman: Several of my students went; I remember when this happened it was a big deal for them.
Margot Gerritsen: It was a big deal for everybody there, and the atmosphere was intoxicating, I have to say. And for many of the women there this was the only time thus far in their lives, in their professional lives or at university where they were surrounded by many other women, and it was an incredibly positive conference. And then we thought how are we gonna, how are we gonna feed this frenzy.
Russ Altman: This fire.
Margot Gerritsen: Yeah, it was a real fire, but only in one hotspot and that was here on campus, and that wasn’t enough. So we started thinking about scaling it up, and we could have gone to the Moscone Center in San Francisco and said, “We do like a Grace Hopper, so we invite a lot of people to come.”
Russ Altman: Grace Hopper is a very famous lecturer for those unfamiliar.
Margot Gerritsen: Grace Hopper is a very famous conference for women in STEM, and it’s usually held in the fall, and there’s 10,000, 12,000 people there. And its wonderful, absolutely wonderful. But we thought we’d do the scaling in a slightly different way.
We wanted to put a modern conference together, we also were extremely keen on going global. Because for data science one of the amazing things of course about data science is that it’s not geographically constrained. Like the energy industry for example is or many other industries. Everybody can do this. And so it’s really important that we connect people around the world, and there are so many countries where the number of women in data science is less.
So we started this idea of putting together satellite events, and we invited people to become our ambassadors. Now at this point we have nearly 200 events in nearly 60 countries around the world. We’re on six continents, just not Antarctica, but we are working on that, we’re gonna be at Russs Station next time I’m sure.
Russ Altman: This is The Future of Everything. I’m Russ Altman, I’m speaking with Professor Margot Gerritsen about her efforts for I believe it’s called the Global Women in Data Science Group?
Margot Gerritsen: That’s right.
Russ Altman: So has there been any negative to this? Do people feel that in some way you’re making a mistake by highlighting women. Have men been negative on this? Are there women who don’t feel well served with this approach? How has it gone, especially globally where there is a much bigger diversity of opinions and cultural expectations?
Margot Gerritsen: Actually the only negatives, and these are very, very small negatives that we have heard about were here. Not global.
Russ Altman: I see.
Margot Gerritsen: For example, we were for the first time in Japan last year. That’s a very, very difficult country for women in technical fields. And it took us a while, but we’re now in Japan, and the reception to the Japanese conferences have been great, and it’s really making a big difference for the women there. Because they never knew how to find each other. And they are really connecting.
Also we heard the first conference in Saudi Arabia because we are in Saudi, where women were allowed to go to by themselves because there were only women speaking, but that was something we hadn’t thought about. We’re really big in the Middle East. We haven’t heard any negative there. People are so aware that it’s needed for —
Russ Altman: That’s remarkable, that’s really remarkable.
Margot Gerritsen: Yeah, to support women, to inspire women, and to educate everybody, not just men and women this conference is really good. A lot of men listen in, we have a lot of male listeners, we have male attendees to the conference and their eyes open.
Russ Altman: I mean if the content is good, and as you said in your first sentence, this is about high quality technical content, then it would be unwise to ignore it.
Margot Gerritsen: Absolutely, and we are trendy in the sense that we look very important critical problems that everybody talks about. We talk about ethics in AI this last year of course because everybody does. So the only negatives, very small negatives we’ve heard in states is related to why just women, why are you not inviting URM, and of course it’s not just women who are underrepresented into science.
Russ Altman: URM, underrepresented minorities.
Margot Gerritsen: Underrepresented minorities. It’s even worse for underrepresented minorities. What I thought is start with 50% of the population, I don’t know if you listened yesterday to “Marketplace” on National Public Radio, but Kai Ryssdal was saying that there are millions of jobs coming up in the STEM fields that we won’t be able to fill. Well even from an economic perspective therefore it’s just silly to leave 50% of the population behind in this respect.
I thought the lowest hanging fruit were really amongst the women. We’ve talked about this for a very long time and we haven’t moved as much as we’d like. So we’ll focus on that first. Our conference, we’re striving to be extremely diverse when it comes to race, ethnicity. And of course we’re global.
Russ Altman: The nice thing about helping women as your target group is that doesn’t have to rule out the underrepresented minority groups.
Margot Gerritsen: Absolutely not of course.
Russ Altman: Guess what they have women.
Margot Gerritsen: You know it’s amazing, but they do. So we are in that sense very open, and I really hope this is not a feminist movement as such, what we’re really hoping is that we make ourselves redundant.
Russ Altman: So what is it about data science, is there anything about data science that made this a special opportunity? Is it that there was such a big difference in terms of the demographics of the field, that it was just a gaping hole? Is there something about the training that accentuated differences that were perhaps unfair or not leading to a diverse group? I’m sure you’ve thought about this a lot, what was the core root problem? And if we could do it over again, would there has been a way to not have it get out of hand?
Margot Gerritsen: Yeah, it’s so difficult because in computational mathematics, statistics and computing, the computational sciences in general it’s always been very low the number of women. So this is not different. I started computational mathematics when I was an undergrad and there were very few women there, we had maybe 4% women. And at that time I thought okay, by the time I’m old, which I am now according to my 18 year old self, I’m ancient, this would all have been resolved. But it didn’t.
Russ Altman: Much more slowly than many of us expected.
Margot Gerritsen: And you know this Russ, but in computational sciences there is this misconception, and unfortunately this is still going on, and it is still talked about. That you need a certain innate ability to do this well.
Russ Altman: You’re born a data scientist.
Margot Gerritsen: You’re born a mathematician, you’re born a computer scientist. And it so happens that very often people think that men have this innate ability so much more strongly than women. Now we know that that is wrong, there is absolutely no evidence really for that. But if you tell girls that from a very young age that of course it’s very hard to get rid of that misconception. And so that’s one reason.
But the reason why I was so interested in attacking women in data science as opposed to women in some other engineering field, is because data science is such a critical field. If you think about most of the major decisions nowadays, be it in research, be it in politics, or in industry, in covenants, they are made based on data analysis. And if you envision a world —
Russ Altman: So there’s a strategic element here?
Margot Gerritsen: Absolutely. If you envision a world were globally data scientists are gonna have a really, really critical role in decision-making at all levels you want the data science teams who do this to be representative of the population. That’s just the duty we have, we must make sure that it’s fairly represented. Not just between men and women, but also globally, we cannot have Silicon Valley making decisions for everybody in the world.
Russ Altman: This is The Future of Everything, I’m Russ Altman, I’m speaking with Professor Margot Gerritsen about now in the last few minutes this mandate to make data science diverse and inclusive because of its growing influence on decision-making globally. So do you see cultural differences? Do women in some countries have different challenges than they do in other countries? You made references to Saudi Arabia and Japan.
How do you support your sisters in other countries as they try to get local chapters of the global women and data science going? Because it might be that the things that they are struggling with might not be the same things that you’ve had to struggle with, or as a universal experience?
Margot Gerritsen: There is a surprising similarity between most of these countries. In some countries of course it’s much stronger than in others. We are actually not doing so well in the States compared to some other countries, and when most people think about where it is really good, they usually come up with Scandinavia or Holland, where I’m from originally. But actually there it’s also not great, but for other reasons. But in most places it’s that industries and governance labs, computational groups have been dominated by men for the very long time. There is bias against women, there are just not many women around, the pool is low because the problem isn’t really addressed at the core, meaning starting from very early education. And so it’s very, very hard for women to be taken seriously, it’s very hard for women to be promoted. And now we have all these efforts, for example here in the United States, which I applaud very much, is to hire more women, but it’s much more than hiring, you need to promote them also and they’re not promoted in equal numbers.
Russ Altman: Do you have programs to help the leadership of companies understand this challenge? I would guess if it hasn’t been part of their training, they may have no insight as to the opportunity and the problems that it’s causing not to have an appropriate initiative. Is there a way to educate the leadership?
Margot Gerritsen: We do it through our partnerships with industry, so we are partnering with quite a few companies, and then within those companies we talk to leadership. And otherwise we do it by osmosis. What we really want is that the representation of women online, at conferences grows, people start to see, look they are outstanding people, we want to hire those people, we want to promote those people. And then I think we can make a difference that way.
Russ Altman: Does the leadership get it? What are the most effective arguments? If you find yourself in a room with somebody and you say, okay this person doesn’t get it, what are the best mechanisms for you to highlight for them the opportunity?
Margot Gerritsen: Sometimes purely economical, where I say look, “How many open positions did you have, “how hard it is for you to recruit and retain people?” So this is the other thing. And here in the valley most companies are always looking for people, and we are simply saying why are you not tapping in more into the women. Some of the problems for hiring women is that people are judged in a particular way.
Russ Altman: You have to fit a certain stereotype.
Margot Gerritsen: Certain stereotype, and then if you start saying, look maybe you should think about the criteria for hiring, think about the criteria for promotion, then that helps. There is some progress, you know some companies are fantastic, they’re really making progress. Other companies you think oh we’re not doing so well. We can do a lot better at Stanford too.
Russ Altman: This is The Future of Everything, I’m Russ Altman, more with Professor Margot Gerritsen about women in data science next on SiriusXM Insight 121.
Welcome back to The Future of Everything, I’m Russ Altman, I’m speaking with Doctor Margot Gerritsen about global women in data science. So through your efforts in this initiative you must have come acRusss many people and you must have some stories that have stuck out for you in terms of really changing lives. Can you tell us about one or two?
Margot Gerritsen: Yeah there are quite a few, and this is one of the wonderful things about this conference. But what really got me is a story of let’s call her Ankita, she was a young student in India, in a relatively small town in India, and she started listening to our WIDS conference in 2015. It was always her dream to leave India to study, she never really had the courage to do so. But then because of WIDS conference, she said in her words she said, “Because it was such an open and inviting conference.” She felt you know I can do this.
Russ Altman: Oh I’ve just realized WIDS Women in Data Science.
Margot Gerritsen: Data science.
Russ Altman: I was wondering what WIDS was. I’m sorry forgive me. WIDS love WIDS.
Margot Gerritsen: So this girl from the small town in India was listening to WIDS, and she said, “Who’s organizing this?” So she looked us up and she found ICME, this Institute that I was running at the time here at Stanford in computational math, and she applied.
So she said, look that’s what I’m gonna do. So her last year of studies she focused even more on math, she had absolutely no idea whether she would be good enough. She sent us this incredible essay she said I was so inspired by this I never thought I could do this, but I saw these examples, so I thought I’d apply and see. And we looked at her file and we interviewed her, and she is extremely talented so we got her in as a Masters student. And now she works in the valley and she’s a very successful data scientist in the valley. And so her story is mind blowing. And it just shows you how global this can be, because there’s absolutely no reason why somebody from Russia, from New Zealand, from Africa, from South America cannot do this thing. Talent is everywhere.
Russ Altman: And it sounds like just listening to the conference, just gave the extra push to say I can do this.
Margot Gerritsen: I can do this.
Russ Altman: These are people like me, I can do this.
Margot Gerritsen: That’s right, look at all these women having all these amazing technical stories, and talking very openly in some of the more informal sessions of the conference about the challenges they faced. If they can overcome those I want to be part of this.
Russ Altman: So you said she wound up working in Silicon Valley.
Margot Gerritsen: Yes.
Russ Altman: And I wanted to ask you, how is Silicon Valley doing? So Silicon Valley is a famous place for innovation, some of these big platforms and everybody uses every day, Facebook, et cetera, Google, Twitter, Apple, they’re based here. Is Silicon Valley leading the way for women in data science, or are there challenges? What’s the score card if I may say for those of us in our own neighborhood really?
Margot Gerritsen: It’s so hard to give one score for the whole of the valley, because there are so many different companies. We have start-ups, we have more traditional longer, much more long lasting companies. Very large companies, very small companies. Some of them are doing exceedingly well I think, they really take this very seriously, they see the economic benefits, they see also of course that there are great benefits in having more diverse teams.
Russ Altman: It makes it a better company to work at for everybody.
Margot Gerritsen: It makes a better company to work at, and I know this because in ICME for example, when I first became director in 2010 we had five to 10% women in ICME amongst the students, now we have 40, so we built this out. And the culture even in this small place ICME has changed considerably. And you see this in other engineering disciplines where there are a lot of women, like bio engineering is a really great example where the culture is just much more open and inviting. And that’s not because women are generally much nicer, but because there is a nice balance. So bad behavior of any kind cannot really flourish and continue when there is great diversity around, and that makes a big difference, and there’s more openness.
Russ Altman: So I guess I shouldn’t be surprised that Silicon Valley it’s up and down, and it’s based on the individual decisions and resource allocations.
Margot Gerritsen: Exactly, and we hear horror stories. I’ve placed a lot of students in Silicon Valley, and I do occasionally have to call somebody up from a company that we know and say, hey, we placed one of our female PhD students for example in your company and what we hear —
Russ Altman: We’re not getting good reviews.
Margot Gerritsen: We’re not getting good reviews, you’ve got to do something about it.
Russ Altman: That’s actually a huge service to them, I hope they appreciate this?
Margot Gerritsen: They do. And its logical in a way, because a lot of the teams of course that incoming graduate students or incoming undergraduate students after graduation where they start to work are led by people who really don’t have leadership training, and may have grown up in a biased place.
Russ Altman: And what they might not realize but they should is that the students all talk, the alums talk to the students, and if you get a bad reputation that could hurt your hiring for five years, not just for five months or five weeks.
Margot Gerritsen: Absolutely, yeah. So I would say overall, if you want the grade, I think we are not higher than a B in Silicon Valley, and I wish it was different, but some companies get a really great scorecard. There is still a little bit, because it is very much a male dominated, young male dominated environment in Silicon Valley. Sometimes people say it’s a bit of a fraternity culture, and you do see this in some companies.
Russ Altman: And it’s been shown in some popular shows on television.
Margot Gerritsen: That’s right, that’s right, and I wish looking at these shows you could say this is absolutely exaggerated, but it’s —
Russ Altman: Sometimes it hits a little close.
Margot Gerritsen: It hits a bit close here.
Russ Altman: This is The Future of Everything, I’m Russ Altman I’m speaking with Margot Gerritsen about women in data science. And really this amazing organization Global Women in Data Science, and it’s more than a conference. So what are the other things that create the pallet of offerings for the community?
Margot Gerritsen: Well when we started seeing this movement come out of this conference with all these ambassadors and representatives all over the world, we wanted to have a presence throughout the year. So we started a podcast series, so I have my own podcast series.
Russ Altman: Oh, well let’s have a shameless plug for the competition. What’s it called?
Margot Gerritsen: The competition. It’s the WIDS Women in Data Science Podcast, and you can get it from our website, WIDSconference.org.
Russ Altman: Wonderful, wonderful.
Margot Gerritsen: But we also have a Datathon.
Russ Altman: Datathon?
Margot Gerritsen: A Datathon, it’s sort of a hackathon around data. And we have this every year.
Russ Altman: So what would be a typical Datathon?
Margot Gerritsen: It’s always around social good, we found that is one of the things that really attracts a lot of women. We work with Gaggle on this, and one of the reasons we really wanted to extend to me Datathon or hackathon, is because we know that very many girls are very hesitant participating in these sort of hackathons. And we wanted to lower the barrier to that. And so we have a rule that 50% of every team needs to be women, so we have mixed teams, and they do well, but also all women teams do very well.
Russ Altman: Can you give an example of one of the social good projects that you might have thrown out into a hackathon?
Margot Gerritsen: So this last year we had data from Planet Labs, and we had a challenge where they had to identify palm oil plantations around the world using satellite data. So that was interesting —
Russ Altman: So this I know is a huge environmental problem.
Margot Gerritsen: A huge environmental problem, and we wanted to see how fast palm oil plantations were encroaching on the jungle for example, so deforestation was a big thing here. The team that won, one of the teams that one was a mother and daughter team, which was fascinating.
Russ Altman: Oh my goodness, there’s a story.
Margot Gerritsen: It was a wonderful story. The other really interesting think we had a mixed team, a Nigerian woman and a Swedish woman hooked up through the WIDS network, and worked on this together. So that again shows you that you can have these global connections. We had a Spanish girl working with people in the UK. And so it’s fascinating.
Russ Altman: So when you say team, this is sometimes a team of two, sometimes a team of 10 or 20.
Margot Gerritsen: No it’s a two or three mostly.
Russ Altman: It’s always pretty small?
Margot Gerritsen: Yeah, or four most.
Russ Altman: And they can literally meet each other. So that suggests to me that you have other activities, because they had to meet. So you must have some social networking going on as well?
Margot Gerritsen: We have social networking going on, and when we launched the Datathons we are allowing participants to connect each other and the ambassadors help out with that too. Our 250 ambassadors or so around the world. And now the other thing that we’re doing and I’m very, very excited about this, we are reaching out to middle school and high school, packaging up not just the talks that we have that we think would be extremely interesting to middle schoolers or high schoolers, and a lot of the topics that are discussed are interesting to them. But also some of the podcast interviews that we have, and some other materials.
Russ Altman: So you can curate the material for appropriate presentation to an eighth grader?
Margot Gerritsen: And we give a nice little package to teachers and say, “Hey, here are things around data science, “they’re technical mostly in nature. “It just so happens that all of it is done by women. “But show it to the class, “both the men and the women.” And the more we can see this. And the other thing that we’re doing is we’re making sure that there are many many, many of our videos online available. So that when you search for say, a talk on artificial intelligence, that you have a fairly high chance of finding a talk by a woman.
Russ Altman: Ah, on the first page of Google hits.
Margot Gerritsen: We want videos also by women, because if you look in the past on artificial intelligence for example, people that were interviewed on various podcasts, mostly men. Talks that are being promoted, mostly talks by men. But now there are quite a few hundred talks by women out there through the WIDS network, not just our talks, but talks in all of these regional events. So any girl can find a role model around the world through that selection of videos.
Russ Altman: A fantastic organization that’s really literally changing the face of data science in the world. Thank you for listening to The Future of Everything. I’m Russ Altman, if you missed any of this episode listen any time on demand with the SiriusXM app.