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Tune in, and get inspired with WiDS

Get to know the women behind the data science. Leading women in data science share their work, advice, and lessons learned. Hear about how data science is being applied and having impact across a wide range of domains. Join hosts Margot Gerritsen and Chisoo Lyons in the open and informal conversations with our many inspiring guests.

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EP. 23

TOPICS: Data Generation/Collection, Data Science as a Career

Francesca Dominici and Rachel Nethery, from Harvard University’s T.H. Chan School of Public Health, are working to inform public health policy through research at the intersection of environmental health science, data science, climate change and health policy.

EP. 22

TOPICS: Data Generation/Collection, Data Wrangling

Manisha Desai, professor of medicine and biomedical data science at Stanford University, shares some insights about the challenges and progress of current COVID-19 clinical trials.

EP. 21

Newsha Ajami, director of Urban Water Policy with Stanford University’s Water in the West, focuses on improving urban water systems through interdisciplinary research combining data science, engineering and public policy.

EP. 20

TOPICS: Algorithms, Data Generation/Collection

Andrea Gagliano, Head of Data Science, AI and Machine Learning at Getty Images, works at the intersection of art and technology — using machine learning to inspire creativity, and the arts to comprehend technology.

EP. 19

Ya Xu, head of LinkedIn’s global data science team, explains how the company takes responsibility for data privacy and creating economic opportunities for all of its members.

EP. 18

TOPICS: Algorithms, Data Science as a Career, Values

Susan Athey, Economics of Technology Professor at the Stanford Graduate School of Business, brings an economist’s expertise and perspective to machine learning and data science.

EP. 17

Montse Medina, a successful data science entrepreneur, is currently a partner at Deloitte in Spain responsible for the firm’s advanced analytics and asset-enabled business.

EP. 16

Timnit Gebru spoke to us when she was a research scientist and technical co-lead of Google’s Ethical Artificial Intelligence Team. In this episode, Timnit explains the importance of advocating for diversity, inclusion and ethics in AI.

EP. 15

TOPICS: Data Science as a Career, Values

Christiane Kamdem and Lama Moussawi discuss the importance of role models, mentors and giving back to empower women and girls to pursue data science careers.

Podcast Hosts

Professor Margot Gerritsen

Margot Gerritsen was born and raised in the Netherlands, and after getting her PhD at Stanford, she.spent time in New Zealand in the Department of Engineering Science at the University of Auckland, returning to Stanford in 2001 as faculty member in Energy Resources Engineering. Margot specializes in the development of computational methods for renewable and fossil energy production. She is also active in coastal ocean dynamics and yacht design, as well as several other areas in computational mathematics including search algorithm design and matrix computations. Margot also Chairs the Board of Trustees for SIAM (Society for Industrial and Applied Mathematics).

Chisoo Lyons

Chisoo was born in South Korea and grew up living in Indonesia, Germany, and Jordan, before moving to the US. She attended University of California Berkeley and received a bachelor’s degree in Industrial Engineering & Operations Research and continued with her master’s focusing on Operations Research. Living in different parts of the world cultivated her ever-present curiosity about people and cultures. Chisoo worked for FICO, a global analytic and decision management company, applying data science to developing, consulting, and implementing decision support solutions for her clients. Her corporate career culminated in leadership roles managing innovation and lines of business. Now, as the Chief Program Director of Women in Data Science, Chisoo is thrilled to be in service of a global community of amazing women doing extraordinary work in the field of data science.