Skip to main content

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.

  • Topic(s)

  • Sort By

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.

EP. 14

Sherrie Wang, spoke to us when she was a fourth year PhD student at Stanford’s Institute for Computational and Mathematical Engineering (ICME), explains how she applies machine learning methods to help solve global food security challenges. Sherrie has since completed her PhD, and is working as a postdoc at UC Berkeley, working on machine learning for sustainability.

EP. 13

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

Marzyeh Ghassemi, an assistant professor at the University of Toronto, is focused on Healthy ML—applying machine learning to understand and improve health.

EP. 12

TOPICS: Algorithms, Data Generation/Collection

Shir Meir Lador, data science team lead at Intuit in Israel, develops machine learning models for security, risk and fraud in products like Quickbooks, Turbo Tax and Mint.

EP. 11

Natalie Evans Harris, a leader on ethical and responsible use of data, explains how building trust through a shared vision and data “code of ethics” is essential to promote both innovation and privacy.

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.