Women In Data Science



Hear from women leaders across the data science profession, as they share their advice, career highlights, and lessons learned along the way. This podcast is brought to you by the Stanford Institute for Computational & Mathematical Engineering (ICME) and the Stanford School of Engineering. Generous support for this podcast and other Women in Data Science initiatives has been provided by Intuit, Microsoft, SAP, Walmart Labs, and Western Digital.


  • Karina Edmonds | Building bridges between business and academia

    07/10/2021 Duration: 38min

    Though Karina showed an early aptitude in math, her high school counselor advised her against pursuing an engineering degree. She ignored his advice and went on to earn her undergrad degree in mechanical engineering from the University of Rhode Island and a PhD in Aeronautics from Caltech. She landed her first job as a speech-to-text engineer at TRW where she was awarded her first patent. She then moved on to technology transfer as a patent agent at the Jet Propulsion Lab. She bounced back to academia, managing corporate partnerships for Caltech, and then returned to industry as Google’s University Lead for Google Cloud AI/Machine Learning. She is now at SAP as Global Head of Academies and University Alliances, continuing to connect industry and academia.In her diverse career spanning business and education, she has seen increasing power concentrated in big tech companies through their ownership of immense datasets and computational power. Companies are also attracting talent away from universities that are n

  • Fatima Abu Salem | Applying data science for the public good in Lebanon

    09/09/2021 Duration: 33min

    Fatima Abu Salem grew up in Lebanon and has focused her data science research on addressing critical challenges in the region, including problems around the Syrian refugee crisis, the water quality in Lebanon and their irrigation requirements for farmers. Fatima explains that her life journey is really enshrined in the conflict of the region. She was born to a Lebanese mother and a Palestinian father who had refugee status so she inherited this status. This meant there were many restrictions on what career she could pursue and her dream to become a doctor was out of reach. But she was good in math so majored in mathematics at the American University of Beirut and one of the only jobs available to her as a Palestinian was able to become a high school teacher. She taught for a few years but realized it wasn’t what she wanted to do with her life. At the time, she was working on her master’s thesis in Algorithmic Number Theory at the American University in Beirut and decided she wanted to pursue a PhD. She emaile

  • Louvere Walker-Hannon | Gaining skills and overcoming barriers to a career in data science

    12/08/2021 Duration: 48min

    Louvere Walker-Hannon has worked at MathWorks (the company that makes MATLAB) for over 21 years, where she’s also a STEM Ambassador. She studied biomedical engineering as an undergraduate at Boston University and did graduate work at Northeastern University in geographic information technology with a specialization in remote sensing.She loved working with MATLAB as an undergraduate and when MathWorks came to the career fair when she graduated, she sought them out, got an interview, and has been working there ever since.She says there are both technical and non-technical skills that are valuable in the field of data science. Technical skills include coding, some programming, a foundation in mathematics, some statistics, and in some cases physics. Non-technical skills are also very important. It’s critical to be able to communicate your findings clearly using a variety of techniques. She says stay away from technical jargon and communicate as if you’re having a conversation. A second important skill is active l

  • Menglin Cao | Data science in fintech and financial services

    15/07/2021 Duration: 29min

    After she earned her BA, MA, and PhD in economics from the University of Maryland, Menglin Cao spent six years at Fannie Mae before joining Wells Fargo. Over the past 15 years, she has seen a major shift in how financial institutions use data to drive business decisions. In the past, many business decisions were based upon the experience and judgement of senior executives, but today every decision must be backed up by data and analytics. Many aspects of the financial services that leverage data are great candidates for AI and ML models, such as customer experience, revenue generation, and risk management. She wants to ensure they build models that are fair, explainable, interpretable, and able to generate value.She offers several examples about best practices for success.To be useful, the data must first be focused on a foundational business question. “Data can tell many stories, but until you bring the story to the businesses and they can relate the story to a question, experience or concern that they have,

  • Karen Hao | Covering AI and Ethics Washing in the Tech Industry

    10/06/2021 Duration: 34min

    Karen Hao trained as a mechanical engineer and then joined a Silicon Valley startup, thinking that technology was the best means to create social change. While surrounded by smart people who were also passionate about using technology for social change, she soon discovered there were no incentives or pathways to accomplish this. “When you're inside a technology company and you're thinking this is going to help change the world, you're often blind to unattended consequences of your work,” she says.She decided to transition to a career in journalism where she could create social change by raising awareness about social impacts of technologies like AI, and how big tech companies engage in “ethics washing” to protect their profits. She is intrigued by the way that incentives shape the work that is done at a systemic level. She says every tech giant suffers from issues at the systemic level where there are people who deeply care about ethics within the organization, but it doesn't mean the company is willing to ch

  • Cecilia Aragon | Aerobatic Pilot, Author and Data Scientist

    06/05/2021 Duration: 32min

    The multi-talented Cecilia Aragon is a data scientist, professor, author and champion aerobatic pilot. In this podcast, she explains how learning to fly gave her the confidence to pursue her career in human-centered data science and as an author.Her book, Flying Free: My Victory Over Fear to Become the First Latina Pilot on the US Aerobatic Team, is the story of how a timid daughter of immigrants who had terrible phobias overcame her fears to become a champion pilot. Learning to fly and excelling at it helped her overcome emotional barriers from childhood when she was fearful and doubted her abilities.In her mid-20s, she was pursuing her PhD and felt a lack of confidence, so dropped out of the program. “It wasn't that I had failed life, but I was living a very narrow life. I was just saying no to everything that might be exciting or interesting. And I saw my life stretching in front of me as incredibly narrow. A colleague offered me a ride in a small airplane…It suddenly occurred to me that living life too sa

  • Kristian Lum | Applying Statistics to Promote Fairness and Transparency

    09/02/2021 Duration: 30min

    Kristian’s interest in statistics and algorithmic fairness has taken her on a winding career path from academia to business, to public service, and back to academia. As she has made different career changes, she didn’t decide between academia vs. industry vs. non-profit, it was more about the problem she was interested in working on at the moment, and what else is happening in her life. After she earned her PhD in Statistical Science from Duke University, she worked as a research professor at Virginia Tech where she did microsimulation and agent-based modelingin a simulation lab. After that, she tried a data visualization and analytics startup called DataPad that was quickly acquired. When she was thinking about her next step in her career, she wanted to do something with social impact.She was fascinated by the work of the Human Rights Data Analysis Group (HRDAG) that was applying statistical models to casualty data to estimate the number of undocumented conflict casualties. She spent a summer working for HRD

  • Lillian Carrasquillo | Using Human-Centric Data Science at Spotify

    03/12/2020 Duration: 38min

    A data scientist by training, Lillian brings a passion for human-centric machine learning and algorithmic effects to her role leading Spotify’s Personalized Home team. She also talks about the experience of leading her team from home during the pandemic.Lillian’s team of data scientists and user researchers collaborate with a mixed-methods research approach to try to discern user needs and create the best matches between listeners and creators. They observe and analyze user behaviors to develop product insights. Her team’s user researchers develop qualitative insights about user needs and then work with the data scientists to figure out the scale of this need or behavior. “What are users telling us they actually need in that moment by doing certain actions, by looking and exploring at certain recommended items?” She sees that Spotify can have a big impact on inspiring new experiences between listeners and the creators of music and podcasts. By sharing music and podcast stories with the world, Spotify often ma

  • Femke Vossepoel | Applying Data Assimilation Tools to COVID Forecasting Models

    27/10/2020 Duration: 39min

    After earning her PhD in Aerospace Engineering at Delft, Femke spent several years in oceanography, climate research, and subsurface modeling. She developed an expertise in data assimilation that she's now applying to improve COVID-19 pandemic forecasting models. Femke explains that data assimilation originated in weather forecasting, where a model is updated with the current day’s weather observations to provide a more accurate forecast for the next day. Data assimilation tools tune the model to provide a more accurate forecast. This concept can be applied in many areas including financial markets, the oil industry, and for COVID-19 research.To help improve COVID-19 forecasting, she is using a compartmental model where there are compartments for different groups: those susceptible to COVID-19, those exposed to it, those infected, those who recovered, those in quarantine, and those who are deceased. The model is like a set of boxes, and the transition from one box to the other is governed by an ordinary diffe

  • Francesca Dominici + Rachel Nethery | Using Data Science to Study Air Pollution Effect on COVID-19 Outcomes

    20/08/2020 Duration: 34min

    With the onset of the COVID-19 pandemic, Francesca Dominici and Rachel Nethery saw a way to connect the research they were doing on air pollution and health with the pandemic. They are studying the effects of air pollution exposure on different causes of hospitalization to see if pollution could increase a person’s vulnerability to COVID-19. While the research is at a preliminary stage, there is a lot of information that points towards the possibility that long-term exposure to air pollution could increase the mortality risk for COVID-19. Since this research is so important and timely, they have sometimes felt under pressure from the media and from high level government officials (from both parties) to answer questions with certainty about how many lives would be saved by reducing air pollution. They say it can be stressful to talk with certainty about their preliminary findings because the research is ongoing. But as a scientist, you should do your work with the utmost rigor and be able to communicate the un

  • Manisha Desai | The Importance of Data Integrity in COVID-19 Clinical Trials

    16/07/2020 Duration: 38min

    Manisha Desai is a professor of medicine (research) and of biomedical data science, and director of the Quantitative Sciences Unit at Stanford University. She is an expert in the design and analysis of clinical trials and epidemiologic studies across multiple diseases, including COVID-19. In this podcast, she provides some insights into the challenges and progress of COVID-19 clinical trials.When COVID happened, Manisha knew her team’s expertise in clinical trials would allow them to get up to speed quickly to study this new disease. So, she augmented her team with additional data science experts to focus on trials to identify safe and effective drugs to help prevent hospitalization and disease progression.“This whole period has been so anxiety-inducing for so many of us, it's actually been nice to focus that energy and anxiety towards work that could be part of the solution to some of the crisis,” she says. In studies so far, they have learned that there's a real disparity in outcomes by ethnicity and race a

  • Newsha Ajami | Improving Urban Water Systems Through Data Science, Public Policy and Engineering

    03/06/2020 Duration: 33min

    Newsha Ajami is a hydrologist specializing in sustainable water resource management, water policy, the water-energy-food nexus, and urban water strategy.When she was studying hydrology in grad school, she took a water policy class that changed the trajectory of her career. “I would say that was one of the most important events in my professional career. I realized that laws and policies are what change the way we manage resources,” she says. All the data optimization and modeling means nothing unless you can understand the policy layer imposed on how our natural systems operate. This interdisciplinary approach guides her research at Stanford’s Urban Water Policy group where she brings together expertise in hydrology, data science, engineering, public policy, human behavior and economics to improve urban water systems. Newsha explains that we’ve spent a lot of time focusing on building more capacity to meet increasing demand for water because our 20th century approach to water resource management has been very

  • Andrea Gagliano | The Intersection of Arts and Technology

    14/05/2020 Duration: 31min

    When Andrea was studying math as an undergrad, she was required to take an arts class in order to graduate, and soon discovered that she loved poetry. She learned that the process of writing a poem was often similar to solving a complex math problem—just starting with one part, and then doing one more, and gradually the rest is revealed. She enjoyed it so much that her first machine learning project in graduate school was on poetry/sonnet generation. Andrea wanted to blend technology and art in her career and Getty Images turned out to be the perfect place to combine her two interests. Getty Images curates and manages a huge library of images and videos that are used in editorial news, websites, social media, billboards and more. She started as a data scientist two years ago, and is now leading the AI/machine learning team to develop new tools to help clients more effectively use Getty’s creative assets. She explains that many of their creative clients come to the site and don't have the language to describe

  • Ya Xu | Using Data To Create Economic Opportunities For All Members Of Global Workforce

    15/04/2020 Duration: 40min

    Ya Xu manages LinkedIn’s global team of data scientists that manage data science projects across the company’s products, sales, marketing, economics, infrastructure, and operations. She says the company takes active responsibility over the data they collect to ensure fairness and protect privacy. They are very proactive about how they maintain their members’ trust, either with how they share the data externally or leverage the data to create opportunities.LinkedIn’s fairness mission is that two people with equal talent should have an equal shot at opportunities. To reinforce this, they constantly test new products to determine if a new feature introduces any unintended consequences that might impact fairness. For example, LinkedIn’s referral button allows a job applicant to see if someone they know works in the company and ask for a referral. The unintended consequence is this feature will benefit individuals who have a big network vs. the general population. She says they typically have about 500-600 experim

  • Susan Athey | Bringing an Economist’s Perspective to Data Science

    16/01/2020 Duration: 51min

    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 | Lessons Learned Building a Data Science Startup

    12/12/2019 Duration: 36min

    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

  • Bonus Episode: Margot Gerritsen | How to Get More Women Into Data Science

    14/11/2019 Duration: 28min

    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 Gebru | Advocating for Diversity, Inclusion and Ethics in AI

    23/10/2019 Duration: 36min

    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

  • Christiane Kamdem + Lama Moussawi | WiDS Ambassadors Bring Education and Role Models to their Communities

    03/10/2019 Duration: 30min

    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 Wang | Applying Machine Learning to Solve Global Food Security Challenges

    12/09/2019 Duration: 31min

    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

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