Sherrie Wang | Applying Machine Learning to Solve Global Food Security Challenges

09/12/19 • 31 min

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 where she helped develop the 2019 challenge. Participants were asked to use satellite data to identify oil palm plantations. She felt it was important to bring awareness to this issue as these plantations are contributing to the destruction of the rain forests.

Sherrie’s curiosity has taken her down many different paths so far. If there’s a field she wants to learn more about, she has never hesitated jumping in. She admits these transitions can be challenging and each one comes with a steep learning curve. While she felt overwhelmed early on in grad school, she has learned to succeed through patience and consistent effort.

Her entrepreneurial experience running a tutoring service as an undergrad has also prepared her well for her grad school research. She says that grad school and entrepreneurship are similar in many ways.

“You're creating something new that hasn't existed before, so there are a lot of different directions you can always be going. Realizing that there are all these options out there and it's okay to be brave and pick one and go with it and see how it goes and it's okay to fail, that was a big lesson from that experience,” she says. She is excited about figuring out the new science she can explore that will have an important impact on how we see the world.

She has found her research and teaching so satisfying that she wants to pursue a career in academia. She believes that machine learning and environmental science are critical to our future. “I am optimistic that if we're honest about the sorts of problems we face, then we can collectively, creatively solve these problems. It goes back again to being patient and being consistent,” she says. “The only choice we really have is to keep trying to solve these problems one day at a time.“

RELATED LINKS
Connect with Sherrie Wang on Twitter (@sherwang) and LinkedIn
Find out more about Sherrie on her Stanford Profile
Read more about Stanford ICME
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile
Find out more about Margot on her personal website

Previous Episode

Marzyeh Ghassemi | Applying Machine Learning to Understand and Improve Health

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 data is generated when you go to the doctor because you are sick. However, what matters more than your infrequent doctor check-in is how you're experiencing things day to day, the self-report. She sees a huge opportunity in combining doctor visit data, self-reported data and data from wearable devices that's passively collected from people that consent to their behavioral data being used. We can use all of those different kinds of data modalities to understand what it means to be healthy for all kinds of people.

She also offers valuable insights from her career in data science as a woman, a minority and a mother. She is a visible minority because she chooses to wear a headscarf. “I became comfortable very early on with defending choices that I had made about my life. And that for me really was instrumental in the academic process. Because what is academia if not constant rejection?”

Ghassemi made the decision to become a mother while pursuing her PhD. “As a society we should recognize that having kids is not a career hit.” She felt she was able to have kids and be successful as a graduate student because there was a community around her that was supportive and recognized that having children would enrich her life and experience. She credits having a supportive mentor as being instrumental in making it all work, saying, “You have to choose the race that you can be successful at.”

She wants young women entering the field to know there is no one defined path. She says don't worry about checking boxes. Choose things that you are very passionate about. Find a mentor who's willing to invest in you, and the path you want to take. Surround yourself with good people. It's not the project that makes you successful; it's the people. If you can't trust the people around you, and learn how to work together, you are going to fail. Having the right mentors and having the right people around you should always be your guiding star.

RELATED LINKS
Connect with Marzyeh Ghassemi on Twitter (@MarzyehGhassemi) and LinkedIn
Find out more about Marzyeh on her personal website
Read more about the University of Toronto Faculty of Medicine and Vector Institute
Interview with Marzyeh: Artificial Intelligence Could Improve Health Care for All — Unless it Doesn’t
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile
Find out more about Margot on her personal website

Next Episode

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

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 girls would go to university. Girls were expected to get married and be mothers. So when I applied to universities and I got accepted at AUB with a scholarship, it was a great opportunity that I went for and then things started from there,” she says.

She explains that young girls in the Middle East are very smart and have the quantitative skills, but they need support. WiDS provides support, guidance, and mentorship. “By showcasing role models, stellar female experts in the field, we are encouraging those young girls to not to be afraid to join,” she says. “Events like WiDS help us defy those barriers and those challenges that exist for women.”

Christiane says the majority of the students in her grad school in Cameroon were men, and even now, she is the only woman on a team of five data scientists. After getting her Master’s degree, she started to participate in events to attract more young women in STEM fields. “It's very important to inspire the next generation, and it's important to build a kind of network of data scientists that can be models for the next generation. Because when you have a model, you want to be like this model,” says Christiane.

Both women have helped to host regional WiDS events that are making an impact in their local communities. The most recent WiDS event in Paris had nearly 250 participants. Christiane says she not only gained a lot of technical knowledge about data science, she also heard the stories from many women who had to struggle in order to be where they are now. “It was very instructive to hear that various paths can lead to great achievements,” she says.

Over the past two years the WiDS events at AUB in Beirut have gotten bigger and generated more awareness. In 2019, many more people were interested and wanted to attend the event. “We are seeing a lot of institutional support, and huge support of the local community,” says Lama. “More and more companies are participating, sending their employees, and contacting us to work on initiatives related to supporting women to join the data science field.”

RELATED LINKS

Connect with Christiane Kamdem on LinkedIn
Connect with Lama Moussawi on Twitter (@lama_moussawi) and LinkedIn
Read more about WiDS Paris and WiDS Beirut
Read more about Total and the American University at Beirut
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile
Find out more about Margot on her personal website