Women In Data Science

With a prolific career spanning academia and industry, Susan’s research focuses on the economics of digitization, marketplace design, and the intersection of econometrics and machine learning. She received her PhD at Stanford Graduate School of Business, and taught at MIT and Harvard before returning to Stanford. She was consulting chief economist for Microsoft for six years and the first woman to receive the John Bates Clark medal for her contribution to economic thought and knowledge. Susan sits on the boards of Expedia, Lending Club, Rover, Turo, and Ripple, as well as the nonprofit Innovations for Poverty Action.Throughout her career, she has built upon an early interest in auctions that she developed as an undergraduate at Duke, where she triple majored in computer science, economics and mathematics. Susan first applied her expertise to develop a market-based system for timber auctions in British Columbia that enabled a more efficient allocation of resources that was not subject to trade disputes. The sy

Montse Medina was pursuing her PhD at Stanford’s Institute for Computational and Mathematical Engineering (ICME) when she realized she had a great idea for a company. She left her graduate program to found Jetlore, a prediction platform that empowers retailers with AI-driven content, which was acquired by PayPal in 2018. Montse has since moved back to her native Spain as a partner for Deloitte where she is responsible for their advanced analytics and asset-enabled business.Montse discusses her lessons learned growing Jetlore with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast.She started the business with her thesis advisor and was supported by Stanford’s incubator program StartX that helps students start companies and introduces them to investors. Though they were able to raise money, there were plenty of challenges getting the business off the ground. At first, they didn't want to tell anybody what they were doing. “That already shows that we were very naïve. I

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 she was 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,

Timnit recently completed her postdoc in the Fairness, Accountability, Transparency, and Ethics (FATE) group at Microsoft Research, New York. Prior to that, she was a PhD student at the Stanford Artificial Intelligence Lab, studying computer vision under Fei-Fei Li. She also co-founded Black in AI, an organization that works to increase diversity in the field and to reduce the negative impact of racial bias in training data used for machine learning models.She was born and raised in Ethiopia. As an ethnic Eritrean, she was forced to flee Ethiopia at age 15 because of the war between Eritrea and Ethiopia. She eventually got political asylum in the United States. “This is all very related to the things I care about now because I can see how division works,” she explains during a conversation with Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. “Things that may seem little, like visas, really change people's lives.”Last year, she said that half of the Black in AI speakers coul

Our WiDS Ambassadors in Paris and Beirut discuss the impact of the growing WiDS presence and communities in their countries. Christiane Kamdem, a native of Cameroon and WiDS Ambassador in Paris, is a senior data scientist at the French energy company Total where she analyzes data to create new services and improve market impact. WiDS Beirut Ambassador Lama Moussawi is an Associate Professor at the Olayan School of Business at the American University of Beirut (AUB) where she conducts research and teaches management science. Both women became WiDS ambassadors because they believe that role models, education, and community can make a real impact. “I believe in the vision of WiDS, which is to inspire, educate, and get educated in the field of data science, and to encourage and support more women and girls to join the field,” Lama says during a conversation with Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast.Lama grew up in in Lebanon at a time of war. “It wasn't expected that

Sherrie brings an interdisciplinary and entrepreneurial perspective to her research that she has developed through her work in the fields of computational finance, biomedical engineering, and computer vision. Sherrie explains that about 1 in 9 people do not have access to adequate food. She is using satellite imagery and machine learning to identify and map crops around the world, see where people are most vulnerable, and what interventions or policies have the greatest effect. “There are a lot of problems that technology alone can't solve and we still need to understand the roots of a lot of the problems. That involves talking to people in the earth sciences and agriculture and learning from them. In those conversations, data science just becomes a tool, a very useful tool but it's a tool in the context of some much larger problem,” she explained to Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast.Sherrie has brought her expertise to the WiDS Datathon committee wh

Ghassemi explains how she is tackling two issues: eradicating bias in healthcare data and models, and understanding what it means to be healthy across different populations during her conversation with Women in Data Science Co-Director Karen Matthys on the Women in Data Science podcast. She says that there are built-in biases in data, access to care, treatments, and outcomes. If we train models on data that is biased, it will operationalize those biases. Her goal is to recognize and eliminate those biases in the data and the models. For example, research shows that end-of-life care for minorities is significantly more aggressive. “This mistrust between patient and provider, which we can capture and model algorithmically, is predictive of who gets this aggressive end-of-life care.” Ghassemi is also interested in the fundamental question of what it means to be healthy, and whether that rule generalizes. It requires a different mode for data collection and analysis. She explains that the typical process is that

In addition to her job at Intuit, Lador is a WiDS ambassador in Israel, has her own podcast about data science, and is a co-founder of PyData Tel Aviv meetups. Lador’s team at Intuit focuses on machine learning in security and fraud applications to protect customers’ sensitive financial data from fraudsters and hackers. She and her team use anomaly detection and semi-supervised methods to secure Intuit products and data. “In general, putting AI into products is not an easy task.” But she thinks we need to put a lot of effort into securing our data especially with recent data leaks from Equifax and Facebook. “I think the world is going into that direction with the GDPR and other initiatives. AI has a lot of potential of helping in that domain,” she explained during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Israel has a lot of expertise in the security domain because many young people study security and encryption during Israel’s mandator

During her career at the National Security Agency, Capitol Hill and the White House, Natalie Evans Harris saw that while we collected troves of data, we didn't have strong frameworks and governance in place to protect people in a data driven world. “Data has been used to intrude in our lives. Things are happening based upon data that nobody communicated to the public was actually happening,” she explained during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Data ethics and responsible use of data are essentially about building trust. There's this gap in understanding what sharing data means. Two things have to happen if we're going to build a relationship where people allow their data to be used by a company. Individuals have to trust that what the company is doing with that data is something they're okay with. And the company has to be able to prove that they're being responsible with the use of the data. A company could have the best produ

Ballan brings multiple perspectives to her current work on Connected Cars—drawing on expertise in data science and neuroscience gained during her ever-changing career in academia, entrepreneurship and consulting. Ballan grew up in Turkey where it’s not unusual for women to pursue careers as scientists. In her youth, she was inspired by Mission Impossible TV shows where agents used futuristic technologies like fingerprint recognition and iris detection. She also loved cars. “Those were the things that I was really interested in. And I think my journey started from there,” she explained during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. After studying engineering in college, she pursued her love of cars to work for Bridgestone in Turkey. “I love patterns. And our problem was the tire patterns, how we can identify the right pattern and then balance the car on that right pattern.” She left there for a chance to develop fingerprint recognition

Data science and genetics are closely linked and have been for some time. But now, data science is playing an even larger role in genetics, a trend that is prompting researchers to look hard at their ethical responsibilities, says Chiara Sabatti, a professor of biomedical data science and statistics at Stanford University. As is the case in many other fields, geneticists have access to much more data than in the past, and because it is digitized, it can be mined. “Scientists rely on statisticians to mine this data and help them formulate hypotheses,” Sabatti said during an interview recorded for this year’s Women in Data Science podcast at Stanford. Truly understanding and interpreting this data correctly will become increasingly important for the public good as the relationship between accessibility and privacy continues to grow, she noted. Because there is such a wealth of data, there are potentially thousands of hypotheses that could be explored in some cases, an obviously unworkable situation. Data scient

Career paths don’t always follow a straight line. Just ask Elena Grewal, whose education culminated in a PhD in education, but who became the head data scientist at Airbnb. In some ways, the leap wasn’t quite as daunting as it might sound. Grewal’s training at Stanford was interdisciplinary, including statistics and econometrics. “Often it’s more about words being different than about skills being different,” Grewal said in an interview recorded for Stanford’s Women in Data Science podcast. At one point, she began to study machine learning and initially thought it was very different from the work she was doing. “Then I started looking at what people do in machine learning, and I was like, ‘Oh, it’s logistic regression, it’s clustering analysis. I do that; we just call it something different,’” Grewal says. Whether it’s called data science or not, many different fields have some kind of quantitative component, and people in those fields who are using quantitative skills may well have the background to become a

Consumers know Walmart as a retailing giant that has changed the face of retail in communities across America. But with a data store containing billions of queries and items, it’s also a laboratory for the company’s data scientists and IT professionals who mine and manage it. “We have data scientists embedded in every single team within the company,” says Sonu Durgia, group product manager for search and discovery at Walmart Labs. “Every function at Walmart, from the quality of groceries to the supply chain, has data science embedded in it,” she noted during an interview recorded for the Women in Data Science podcast at Stanford University. Because Walmart’s product catalog is immense, holding the attention of consumers and helping them find what they want to buy is a challenge. “We do not have your attention for the next several hours. We have to show you the right things very, very quickly. So it's a ranking and relevance problem right there, even though it's not coming from a query,” Durgia says. Explaini

Data scientists are involved in a wide array of domains, everything from healthcare to cybersecurity to cosmology. Megan Price and her colleagues at the Human Rights Data Analysis Group (HRDAG), however, are using data science to help bring human rights abusers to justice. The nonpartisan group played a key role in the case of Edgar Fernando García, a 26-year-old engineering student and labor activist who disappeared during Guatemala’s brutal civil war. Price, the executive director of HRDAG, says the investigation took years, but their work led to the conviction of two officers who kidnapped Garcia and the former police chief who bore command responsibility for the crime. “It was one of the most satisfying projects that I’ve worked on,” she says. Price discussed the case in more detail as well as other cases she’s worked on over the years and the role data science played in an interview recorded for the Women in Data Science podcast recorded at Stanford University. For a recent project in Syria, Price’s grou

Fiber optic cables that convey data at high speeds across the globe area is a well-known feature of modern technology. Now, university data scientists have found a unique use for them: monitoring earthquakes.Distributed across Stanford’s telecom infrastructure, the cables have become a seismic array that has already collected data on over 1,000 Bay Area earthquakes, says Eileen Martin, a recent alumnus of Stanford’s Institute for Computational and Mathematical Engineering, now Assistant Professor at Virginia Tech, whose research is focused on seismology. Martin and Nilah Monnier Ioannidis, a postdoctoral scholar concentrating on data science and genomics at Stanford, sat down to discuss the pivotal role of data in their research for the Women in Data Science podcast. Despite coming from different fields, both researchers tout the importance of data in academic research. Genomic sequencing requires vast amounts of data, but privacy concerns mandate important restrictions, Ioannidis says. Consequently, she is

“Fail fast” has become something of a mantra in Silicon Valley. But Janet George, the chief data officer of data storage giant Western Digital, has an amendment to that conventional wisdom: “Fail privately.”She suggests that failing privately allows you to open yourself up to discovery and exploration in a safe setting where you are able to take risks. “Carve out time for yourself so you can fail privately. So, you take 20 percent of your time in big initiatives you feel you can really contribute to, but take 20 percent of your time [(where you can])fail privately.” George, who has worked for some of the most important companies in the technology industry, shared this piece of advice, her career trajectory and the role of data science in the storage industry for the Women in Data Science podcast at Stanford University. Although the fear of failure is natural, it should never become a reason to avoid risk, she says. Taking an executive role at a storage company was a risk for George because she knew little abo

When Jennifer Widom began her career in computer science, it was a relatively narrow and specialized field. Three decades later, computer science has become an interdisciplinary field that touches on broad swaths of society and promises solutions to global problems such as healthcare and sustainability, she says. “Computer science used to be a niche. But (it) has become much more broadly used, broadly applicable across all fields. Instead of it just being a narrow study of software and hardware, it's now a lot about what you can use that software and hardware for in other fields,” says Widom. Indeed, learning about the relationships between math, computers and music prompted Widom to make a radical career change. Her undergraduate degree is in music, and she was on a path to become an orchestral trumpet player. But a course focused on computer applications for music was so intriguing she shifted her studies, eventually becoming a computer scientist and the dean of the School of Engineering at Stanford. Incre

Be yourself” was just one of the many career tips Caitlin Smallwood shared during a conversation with Stanford professor and Women in Data Science podcast host, Margot Gerritsen. Smallwood, vice president of data science and analytics at Netflix, urges up-and-coming data scientists to explore “the avenues and nooks and crannies” of the discipline and avoid limiting themselves to the most obvious paths. Smallwood is passionate about data-driven content and predicts that deep learning will continue to propel advances in applied data science in the future, specifically in the area of machine translation. It will take some time, she says, but machine translation would allow users to watch a movie or video and understand the subtleties of language and culture at a deeper level through nuances in inflection appropriate for different languages. Smallwood is interested in the ways that data science guides content and helps people “understand regions and cultures around the world through storytelling.” She enjoys the

Attaining tenured status at a major university is often the culmination of an academic’s career; giving it up is unthinkable for most. But after 10 years at UCLA, Jennifer Chayes was offered a job at Microsoft. The offer, she says,“scared me to death,” but she took the job and is now managing director for Microsoft Research in New England, New York and Montreal. “There are brass rings that come along,and they always come along at the most inopportune times,and they look really scary, but I believe that we should grab them when they come along,” Chayes says during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Chayes is a big advocate of eliminating biases in search algorithms and believes that data scientists have “the opportunity to build algorithms with fairness, accountability, transparency and ethics, or FATE.” FATE, a group that formed at one of Chayes’ labs, works to address inequity in the field. In one particular instance, the group