Needed: More women in data science
A recent gathering at Stanford on the emerging science of big data turned the usual gender ratio of science conferences on its head.
The rise of big data is visible almost everywhere. Using huge databases too unwieldy for conventional computing, data scientists are extracting insights that change the way we see the world.
From Internet search algorithms to understanding and targeting retail customers, from medical research and health care, financial markets and weather prediction, the new insights available from big data have pushed data science into the top tier of cutting-edge research.
What big data needs now is for more women to move into the field, said Persis Drell, dean of Stanford’s School of Engineering. Drell and other female scientists said they’ve long had the experience of being surrounded by men on all sides at science conferences. So the inaugural Women in Data Science conference held at the Arrillaga Alumni Center Nov. 2 was noteworthy not only for its depth of experts but also because it was a rare all-female science meeting.
The daylong event followed the 2015 IEEE International Conference on Big Data, which took place in Santa Clara, California, Oct. 29 through Nov. 1. The Stanford conference included 400 women from 80 companies, 30 academic institutions and a couple of national laboratories. One third of the participants were students. Many of the companies represented were household names – Google, Netflix, Salesforce and Walmart, among them.
The scientists who spoke about their research, from Stanford and across the country, were all women. Ditto for the panelists who answered questions. In addition to delving into the latest research, the conference provided opportunities for participants to network.
“This is a dream for us, to organize a conference like this,” said Margot Gerritsen, director of Stanford’s Institute for Computational & Mathematical Engineering, the main sponsor of the event.
Gerritsen and Drell laid out the business case for more women in the data science world. “People are screaming for good data scientists,” Gerritsen said. “Not tapping into 50 percent of our population of our talent would of course be a very silly thing to do,” Gerritsen said, adding that understanding data science requires diversity.
The talks, too, covered a broad spectrum; among the speakers was Fei-Fei Li, an associate professor of computer science, who described teaching computers to look at photos and describe what’s happening in the scenes. Her team built a database of 15 million photos for the computer’s training.
Drell encouraged the scientists to support the next generation of data scientists, allowing them to explore the intellectual path that grabs their attention.
Diversity is the talk of Silicon Valley, Drell said, but what often gets left out is why it’s important.
“When there is a difficult challenge to address, and our world is full of difficult challenges, we need a diversity of thought, a diversity of approaches, a diversity of styles to get to the solutions – and that’s why we need diverse teams,” she said.
“Now let’s acknowledge sometimes diverse groups can be harder to manage,” she added. “It’s really great to manage a team where everybody agrees with you, but you will not get to the right answer.”
Videos of several of the Women in Data Science conference presentations are available on YouTube.