EPISODE SUMMARY Sherrie Wang, 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. EPISODE NOTES 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.

EPISODE SUMMARY 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. EPISODE NOTES 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."