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 differential equation. Then in those equations, you have parameters, which are typically not well-known. The data assimilation approach is to work more from the “outside in” instead of from the “inside out”. So, if you know the number of people that have died since the start of COVID, then according to this data, you can determine what the parameters would have looked like three weeks ago. With this type of inverse modeling, you can actually tune the parameters in that compartment model, and find the most likely reproduction number or the likely number of infected in the first place. The approach of having these simple relationships between the different compartments is a good framework for a very complex process. However, you cannot expect the data to tell you the story if you don’t have any prior domain knowledge. In order to take their research to the next level, it will be critical for Femke and her colleagues to collaborate with the medical experts that built the models who know how to express certain relationships.As she has transitioned from one field to another in her career, Femke has needed to learn how to apply her expertise to entirely different research areas. She says one of the most important skills she has developed is to ask a lot of questions and not worry about being wrong and she advises young researchers to do the same. Sometimes those questions can help people already in the field think differently, and lead to new insights. Femke’s experience as an endurance athlete has also taught her valuable lessons for her work as a scientist. “People who excel in sports lose more races than they win. You have to make mistakes and fail, that’s the way you actually grow.” It also teaches you perseverance, to hang in there when it gets tough, and be happy with small increments of your own progress rather than always comparing yourself to your competitors.RELATED LINKSConnect with Femke Vossepoel on LinkedInFind out more about Femke on her TU Delft profileRead more about TU Delft Civil Engineering and GeosciencesConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Women in Data Science

Femke Vossepoel | Applying Data Assimilation Tools to COVID Forecasting Models

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 uncertainties and the possibility that there's bias in the results, knowing this is still valuable scientific information and should be considered within a larger body of evidence to make policy.Francesca feels optimistic that this research is going to make a tremendous positive contribution and the most important thing is to do work that matters and has an impact. She thinks that the field of public health is getting much more attention and respect. “This is the first time we’ve had the opportunity to speak with high-ranking senators about the importance of issues regarding climate change, health disparity, data science and air pollution. There is a silver lining in recognizing the value of data and the value of science in a way that has never been done before,” she says.Rachel agrees. “I feel privileged to be starting my career at a time when suddenly everybody in the US knows what an epidemiologist is and appreciates the value of health data science. Policy makers and the media are very interested at this moment in what data can tell us about our future with COVID. I hope that moving forward this might ignite curiosity about other ways that we could integrate health data science into our policymaking and our lives,” says Rachel. “One of the most important things that government can be doing right now is to collect data in a systematic and reliable way about health and public safety.”When asked what she would put in an executive order in the area of health, Francesca says she would first lower the level of fine particulate matter allowed in the National Ambient Air Quality Standard, especially during the pandemic. Second, she would ensure we are not just cleaning the air for the wealthy as poorer minorities are most affected by air pollution. And finally, she would put accomplished women scientists at the head of all our health agencies: NIH, CDC, FDA, and EPA, because they understand the issues and would get things done.RELATED LINKSConnect with Francesca Dominici on Twitter (@francescadomin8) and LinkedInFind out more about Francesca on her Harvard ProfileConnect with Rachel Nethery at HarvardRead more about Harvard T.H. Chan School of Public HealthConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Francesca Dominici + Rachel Nethery | Using Data Science to Study Effects of Air Pollution on Severity of COVID-19 Cases

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 and they are trying to determine if that’s due to genetic, cultural, or environmental factors. They are also launching trials to figure out questions about immunity, seroprevalence and incidents. Clinical trials are the gold standard for identifying safe and effective therapies. Since these trials require a lot of time and rigor, the findings have high internal validity. In a typical clinical trial, there is a sequential plan which starts by designing the study, setting up a data and safety monitoring board, database capture, and analysis plan. It's a totally different in a pandemic when you want to launch all of these events quickly, so developing the analysis plan and designing the study don't happen sequentially. “You're talking to the FDA and the IRB at the same time that you're developing the protocol, just as the ideas are sort of coming together. Everyone needs the study launched, we're all sheltering in place and solutions need to be found, so everyone is rushing to do this,” she says.She cautions that we need to be careful. “These protocols are in place for the safety of the patients, and the integrity of the data and the study. The worst thing would be spending all these resources, having patients contribute to the trial, and then at the end of the day having inconclusive evidence,” she says. People are anxious about finding answers, they want to publish and disseminate their findings and be the first to solve the problem. And as data scientists, it’s critical to ensure the rigor, that the study is designed well, that the limitations are understood and that there's an appropriate interpretation of the findings. All of the misinformation that has been going on during this pandemic can contribute to further loss of lives.Collaboration on COVID-19 research across groups is essential because we need to pool data. When you combine data across institutions and countries, we need a level of certainty about who is collecting what, how often, on whom, under which policy. This will really impact how and if you even can pool the data, and how you can interpret it. She says one of the most important foundations to the success of the research is ensuring that the data has integrity. People spend so much time spent thinking about the models but we need to get the data management process right before applying the models. There is not currently one centralized, robust data repository in the US that makes it easy for researchers to study questions when they arise. Manisha says people are trying to get these registries up and going quickly, and it's not easy because we ascertain data differently across different sites and countries. This is a very worthwhile investment to put time into thinking about how to collect data in standardized forms. Manisha has seen an enormous response to solving the COVID crisis. She is optimistic that we will identify safe and effective treatments and a vaccine on the horizon. “We’ve learned that the FDA and regulatory agencies can be quite responsive. And maybe even in a non-COVID world, we can learn about how to gain efficiency and have some work arounds when necessary, and get rid of things that might be unnecessary. There will be many lessons learned, not just for COVID, but there will be other pandemics, and we will be able to learn from this experience.”RELATED LINKSConnect with Manisha Desai on LinkedInFind out more about Manisha on her Stanford ProfileRead more about Stanford School of MedicineConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

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

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 one-dimensional and engineering focused. She has learned how it's better to work with nature to get access to clean water, rather than constantly trying to engineer our way out of our natural limitations.As we build future cities and communities, she says we need to be mindful to not impose our outdated thinking. She says we don’t need to build infrastructure like dams or centralized systems that disconnect people from their water resources. Instead, build decentralized systems, green infrastructure and capture and recycle water as much as possible.Newsha’s research helping utilities use data science to improve demand management by increasing the understanding of how and why customers change the way they use water. She recommends that instead of building for more demand that instead we focus on changing mindsets to increase efficiency with the water resources we have.Her research leverages data science across disciplines to understand how water demand patterns are changing over time and then communicates this effectively to decision makers. She wants them to see how we can build cities or communities that are data centric and connect water systems, energy systems and transportation systems to work together more sustainably in the future.RELATED LINKSConnect with Newsha Ajami on  LinkedInRead more about Stanford University's Water in the WestRead more about Stanford WoodsConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

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

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 the types of images they're looking for. Andrea’s team is building models to help clients find images when they don't have the words to articulate their vision. Another machine learning project they are working on is how to identify awkwardly posed “stocky” photos vs. the more realistic photos that clients are looking for.She is currently doing more painting than poetry, and also sees parallels between painting and computer vision. She explains that when you start with a blank canvas, you have to think about shapes, lines, negative space and colors. It’s a similar process to how a machine comprehends pixels and the relationships between colors, contrast, shapes and textures.Andrea created a special photo exhibit for the WiDS Stanford 2020 Conference that illuminates concerns about image manipulation while also posing provocative questions about gender and leadership. In the exhibit, she used machine learning (style GAN) to transform pictures of US presidents, ranging from George Washington to Donald Trump, into female versions of those presidents. The style GAN is a machine-learning model that can manipulate an image in different dimensions, in this case, from masculine to feminine.The project was born out of conversations around Generative Adversarial Networks (GANs), synthetic image generation and concerns about the implications of deep fakes in politics and our culture. She wanted a way to expose that concern in a humorous way. She also saw this as an opportunity to re-imagine our history. What would the world be like today if our presidents had all been women? She says the first response to the exhibit is usually laughter, but then it also sparks questions like: What would it have been like if females had founded the country? What wars would have happened or not happened? What would our constitution be like? How would capitalism have evolved? It catalyzes a conversation about the qualities of great leaders what leadership means through a female lens. RELATED LINKSConnect with Andrea Gagliano on  LinkedInRead more about Getty ImagesConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

Andrea Gagliano | The Intersection of Arts and Technology

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 experiments running concurrently to test new products.One feature that encourages fairness is the push notification that anyone can sign up for that alerts you for when a new job becomes available. That notification is not dependent on your network, and they’ve noticed that this feature especially benefits people who don't have a strong social capital. They share examples like this to help product managers build more socially responsible features.The company has also found that when networks become more diverse, it increases mobility in the labor market. For example, LinkedIn’s Plus One Pledge that encourages people to accept an invitation from somebody who they traditionally would not have connected with has been very successful in opening opportunities for those with less social capital.On the topic of women in the workplace, she encourages women to advocate for themselves. “When I first joined Microsoft, I was the first statistician on the team. People didn't know what to do with me so I just defined what a statistician should be doing,” she says. “It’s up to you to define what kind of role you play in an organization. The more reactive you are, the more that people are going to give you orders. The more proactive you become, the better it is for the company.”Ya has been an individual contributor for most of her career focused on solving a specific problem, but once she became a manager, her perspective shifted. “What excites me now is actually being able to help my team to be more successful. It's almost like there's no way I could solve the problem better than the way that my team can solve the problem. It really excites me when I see their achievement.”She believes that women lead differently. “I have seen women leaders in extremely prominent positions be so humble,” she says. “I think women can be a lot more vulnerable, and it's actually a strength. When we are vulnerable in front of our team, then they relate to us. There's something different about women, and in a very good way.”RELATED LINKSConnect with Ya Xu on  LinkedInRead more about LinkedIn EngineeringConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

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

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 system she developed in the early 2000s is still used today to price almost all of the timber in British Columbia.While at Harvard, she was working on auction models for search advertising when she got a call from Microsoft. Steve Ballmer asked if she could come help them develop their new search engine. She had accomplished many of her academic goals: earning tenure at MIT, teaching at Stanford and Harvard and receiving this Clark medal. “I realized that this could be a good moment in my life to take a risk,” she said.“Being a part of the birth of a search engine, and particularly the search-advertising platform was I think just a transformative experience for my life,” she said. While the Bing search engine was ultimately not able to compete with Google, Microsoft’s investment in the research yielded expertise in machine learning and cloud computing, which is now the company’s most important business.Susan consulted with Microsoft for six years but knew she wanted to continue to pursue her career in academia. After stints at Harvard and MIT, she decided to return to Stanford as she saw it was the best place to collaborate with industry to do cutting-edge research.As Associate Director at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), she brings a social science perspective to AI questions. She explains how in online advertising, you need to understand the system and the economic incentives of the people operating in the system. An engineering perspective sees a database full of advertiser bids that feels static. An economist’s perspective sees those bids as strategic. If you understand the behavior of those firms, and their objectives, you can predict their responses to a change in the system. “It's so important to bring in multiple perspectives. There have been many cases where people have made big mistakes because they only look at it from one particular perspective.”The effectiveness of an application is not determined by the details of the algorithm, what’s really important is that you’re optimizing the right long-term objective. The success of data science, machine learning and artificial intelligence in applications is critically dependent on having domain experts and social scientists that think about long-term objectives and how to measure them.Currently, Susan believes there are opportunities to use technology to tackle inequality problems. She sees potential in using mobile devices throughout the world for education, training, and nudges to guide decision-making. She started the Shared Prosperity and Innovation Initiative at the business school to help social impact firms integrate more AI into their products and services.Another technology that she thinks can help address inequality is Bitcoin. “I think I was the first economist to take Bitcoin seriously. It's fascinating from a variety of angles.” She learned how many people in the world are disadvantaged by an archaic financial system that is operated for the benefit of large businesses and banks in large countries. “If we can move money the way that we can move information, we could actually make a lot of people's lives better off,” she says.She was used to being the only woman in computer science and economics but a lack of role models made it difficult for her to visualize herself succeeding. She felt the need to overachieve to compensate. “It's very stressful to overachieve,” she said. “But I think it translated into more accomplishments because I just didn't think that I had any wiggle room.”Gender was not as much of an issue for her in business because she came in as a defined expert and was not threatening anybody's job. However, in academia she says the power balance is unclear, and there are no rules about who gets to choose. “Being a powerful woman actually is hard. People seem to like their women a little less threatening than I am,” she says. “When I advise women, I suggest having a clear expertise where everybody understands why you're the one who's talking.”RELATED LINKSConnect with Susan Athey on Twitter (@Susan_Athey) and LinkedInFind out more about Susan on her Stanford GSB ProfileRead more about Stanford Graduate School of BusinessConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

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

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. If we had told people, they would have given us advice, we could have adapted faster,” she says.The technology was great but it was difficult to find the right market fit. They had to listen and pivot a lot. She had to learn how to fail, something she was not used to doing. Failure was not in her vocabulary and that was a big lesson. They had to fail to adapt, and the most important thing was to adapt fast.She believes that entrepreneurship is also about luck. “You could be doing everything right and working super hard and being the best entrepreneur, but if you don't get some luck, you won't get the reward,” she says. And if you don’t get that luck at first, she says talk to others, listen to the environment to understand why, and then adapt fast.Montse says it’s no longer necessary to be in Silicon Valley to start a successful company. She believes Silicon Valley is not a place; it’s really a network. For those outside of Silicon Valley who want to be part of that network, she recommends first finding successful entrepreneurs in your region as they likely will have some connections to Silicon Valley. She thinks there are going to be a lot more startups coming from all over the world.It’s important to keep all your options open. When asked about her biggest dreams for herself ten years from now, she replied: “I don't like to box myself into a dream because that forces me into that. I think that there are so many great things that can happen to you just by letting things flow. If I were to think about a goal, then that would probably close other doors that are opening on my sides and windows that I probably would neglect.”RELATED LINKSConnect with Montse Medina on Twitter (@montsechka) and LinkedInRead more about Deloitte Espana and JetloreConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

Montse Medina | Lessons Learned Building a Data Science Startup

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, WiDS now includes a datathon, a podcast that Gerritsen hosts, and ongoing education programs. The results have been, quite literally, life-changing for many.RELATED LINKSConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileConnect with Russ Altman on Twitter (@Rbaltman) and LinkedInFind out more about Russ on his Stanford ProfileRead more about Stanford ICME and Stanford School of EngineeringThis podcast episode was originally published on Stanford Engineering's Future of Everything podcast.

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

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 could not go to NeurIPS because of different visa issues. “And in that 20 seconds, that visa denial, it feels like the whole world is ending for you because you have an opportunity that's missed… Not being able to attend these conferences is much more important than people know.”She has learned through her work with Black in AI that the number one thing we need to do is empower people from marginalized communities, which is why diversity, inclusion and ethics are not at all separate. It’s essential to have a wider group of people in the world determining where AI technology goes and what research questions we pursue. She says the industry has been pretty receptive to her proposals around norms, process and transparency because they are easier to operationalize. However, there are other things like racism and sexism where we need a fundamental shift in culture.She has seen the potential for unintended consequences with AI research. Her PhD thesis at Stanford utilized Google maps data to predict income, race, education level, and voting patterns at the zip code level. She saw some follow up research using a similar methodology to determine what kind of insurance people should have. “And that is very scary to me. I don't think we should veer off in that direction using Google Street View.” She says she wishes you could attach an addendum to your earlier research where you talk about your learnings and your intentions for how the work be used. Timnit is currently working on large-scale analysis using computer vision to analyze society with lots of publicly available images. She says it’s critical that she also spend a lot of time thinking about the consequences of this research.RELATED LINKSConnect with Timnit Gebru on Twitter (@TimnitGebru) and LinkedInRead more about Google AI and Black in AIConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

Timnit Gebru | Advocating for Diversity, Inclusion and Ethics in AI

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 LINKSConnect with Christiane Kamdem on LinkedInConnect with Lama Moussawi on Twitter (@lama_moussawi) and LinkedInRead more about WiDS Paris and WiDS BeirutRead more about Total and the American University at BeirutConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

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

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 LINKSConnect with Sherrie Wang on Twitter (@sherwang) and LinkedInFind out more about Sherrie on her Stanford ProfileRead more about Stanford ICMEConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford ProfileFind out more about Margot on her personal website

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

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

Marzyeh Ghassemi | Applying Machine Learning to Understand and Improve Health

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.” But she thinks we need to put a lot of effort into securing our data especially with recent data leaks from Equifax and Facebook. “I think the world is going into that direction with the GDPR and other initiatives. AI has a lot of potential of helping in that domain,” she explained during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Israel has a lot of expertise in the security domain because many young people study security and encryption during Israel’s mandatory military service. She had the option to do this during her service, but since she already knew she would pursue a career in this area, instead she chose to become a pilot instructor in the flight simulator. “It was a very unique experience that I would probably never get to do.” When Lador was starting her career in data science, she did not know many people in the field. She decided to start a PyData branch in Israel because she wanted to build a professional data science community. “My main motivation was that I wanted to learn and that I wanted to have friends and people to consult with and learn from. And now I have so many data scientist friends because of all this work and it's great. I love it.” She noticed when organizing PyData events that it was much easier to get male speakers. When she would ask a talented female scientist to talk about her work, she would say: “No, I'm not an expert… I'm not ready. I need to learn more… I was like, no, you're enough years in the field. Everyone can learn something from you.” Being a WiDS ambassador was like an extension of her PyData work. “I get to decide what's in the conference and bring the best talks there.” Her experience organizing the PyData meetups helped her know how to create a valuable conference. She sees WiDS as a great opportunity to encourage more women to speak by giving them a platform, but also by bringing all the people together. “Seeing all those women on stage. This gives great inspiration to speak at other events, not just in WiDS. I think this is just an amazing initiative.” RELATED LINKS Connect with Shir Meir Lador on Twitter (@shirmeir86) and LinkedIn Listen to Shir's podcast Unsupervised Learn about PyData TelAviv Meetup Read more about Intuit 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

Shir Meir Lador | Using Data Science to Keep Financial Data Secure

During her career at the National Security Agency, Capitol Hill and the White House, Natalie Evans Harris saw that while we collected troves of data, we didn't have strong frameworks and governance in place to protect people in a data driven world. “Data has been used to intrude in our lives. Things are happening based upon data that nobody communicated to the public was actually happening,” she explained during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Data ethics and responsible use of data are essentially about building trust. There's this gap in understanding what sharing data means. Two things have to happen if we're going to build a relationship where people allow their data to be used by a company. Individuals have to trust that what the company is doing with that data is something they're okay with. And the company has to be able to prove that they're being responsible with the use of the data. A company could have the best products out there, but if people don't trust you or understand what you're doing with the data, then they're not going to trust you to use the data. And then innovation stops. She believes the biggest problem is we do not have a shared vision of what ethical practices mean. We don’t want to put broad impact laws in place to govern responsible use of data when we're still trying to define that vision. To change business practices, we have to change company expectations so that they're not only incentivized to be ethical and responsible in their business models, but they're also penalized when they violate. Harris has been advocating for a data science “code of ethics” to create a shared vision to guide our behaviors, and then start to develop best practices around. Companies are now taking this code of ethics and personalizing it to their businesses around principles like informed consent, transparency, fairness and diversity. Companies then publicize the practices that they're putting in place to align with those principles. That's how you start to create that shared vision. She sees that there's this transformation happening with the relationship between technology and people. For so long, technology has been this very passive thing in our lives, and now with AI and machine learning and all of these uses of data and technology, there's this tension around what technology can do and what humans should do. Until people know and understand what is happening with their data, and until companies can thoughtfully express what they're doing with the data in a very transparent fashion, we will continue to have this tension. She is hoping that this code of ethics can start to ease that tension. RELATED LINKS Connect with Natalie Evans Harris on Twitter (@QuietStormnat) and LinkedIn Find out more about Natalie on her personal website 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 Read more about BrightHive and Beeck Center Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Natalie Evans Harris | Creating A Shared Code Of Ethics To Guide Ethical and Responsible Use of Data

Leading women in data science share their work, advice, and lessons learned along the way with Professor Margot Gerritsen from Stanford University. Hear about how data science is being applied and having impact across a wide range of domains, from healthcare to finance to cosmology to human rights and more. 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.

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