Sydney Hazen, a Privacy Data Scientist at Ford, shares her journey from a college intern to a full-time role. She highlights how internships can lead to job offers and the importance of real-world experience and corporate navigation. Sydney emphasizes applying technical skills with a socially conscious approach, understanding problems before diving into data, and credits her professor and diverse classmates for shaping her perspective. She plans to pursue further education and explore different roles, underscoring the unique position of women in data science and the need for self-advocacy and finding mentors.HighlightsInternship at Ford (8:25)Time at Notre Dame (14:55Influencers (17:54)BioSydney is a Privacy Data Scientist at Ford Motor Company, as a part of the Ford College Graduate (FCG) rotational program. She is a recent graduate of the University of Notre Dame, with a degree in Applied and Computational Mathematics and Statistics (ACMS) and a minor in Data Science. She has a range of experience spanning software development, data engineering, and analytics and is always looking to gain new knowledge in the technology sphere. Connect with SydneySydney Hazen on Linkedin Connect with UsChisoo Lyons on LinkedInFollow WiDS on LinkedIn (@Women in Data Science (WiDS) Worldwide), Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

SummaryListen to Karin Golde, a linguistic expert and AI entrepreneur, as she discusses the rise of large language models (LLMs) and the impact of ChatGPT. Karin reflects on the unexpected popularity of LLMs and the role of OpenAI. The challenges and limitations of LLMs are discussed, including the need for human understanding emphasizing the need for diverse perspectives and cultural understanding in AI development. Karin shares her personal experience of using LLMs and highlights the importance of balancing innovation with regulation in the AI industry. Karin concludes the podcast sharing about her career journey and her recent transition to working as an independent consultant. She offers advice for women considering leadership roles and emphasizes the importance of thinking broadly about one's place in an organization.HighlightsLLMs (1:46)AI systems (3:25)The need for humanness in AI (20:17)Transitioning to independent consultant (28:31)BioKarin Golde, is the Founder of West Valley AI. She helps businesses and technical leaders navigate the rapidly developing landscape of AI and Large Language Models by sharing her expertise which has ranged from executive leadership roles at multiple startups to heading the language engineering division for the AI Data team at Amazon Web Services. Her philosophy is to cut through the hype, collaborate with integrity, and keep a laser focus on providing value to your business. Connect with KarinKarin Golde on LinkedinWebsite West Valley IA Connect with UsChisoo Lyons on LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

 Highlights: 00:02:25 - Colleen’s motivation for writing a book, interdisciplinary collaborations, and explaining advanced mathematical tools in accessible ways.00:08:44 - Journey from biology and social sciences to data science, and the integration of different mathematical tools in solving data problems.00:14:13 - Overcoming imposter syndrome and the value of exploring beyond one's field.00:15:02 - The importance of mentorship.00:23:40 - Coping strategies for setbacks in academia and industry.About the Guest:Colleen Farrelly is an author and senior data scientist. Her research has focused on network science, topological data analysis, and geometry-based machine learning. She has a master's from the University of Miami and has experience in many fields, including healthcare, biotechnology, nuclear engineering, marketing, and education. Colleen wrote the book, The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R.  Mentions:Connect with Colleen Farrelly on LinkedIn Related Links:The Shape of Data: Geometry-Based Machine Learning and Data Analysis in R Connect with UsMargot Gerritsen on LinkedInListen and Subscribe to the WiDS Podcast on Apple Podcasts,Google Podcasts,Spotify,Stitcher

Summary:Listen to the incredible and inspiring journey of Avalon Baldwin’s career journey. A self-described data nerd, she was not only the first in her family to attend college, she went on to get a graduate degree. Today she is an entrepreneur running her own consulting company. In conversation with Chisoo Lyons, Avalon shares how curiosity, mentorship, and coaching made a difference in her life. Highlights: (06:18): Exploring factors like how data is collected, the intention behind collecting a specific data point instead of another one, and how they can influence analysis and interpretation.(08:20): Working with students as individuals and promoting self-agency, as able to influence their own future. (12:02): Avalon describes her journey to become the first in her family to be a college student(32:02): Advice on finding a mentor. About the Guest:Avalon Baldwin master's degree in positive developmental psychology and evaluation from the Claremont Graduate University. She received her bachelor's degree in biopsychology from Mills College,. Avalon's consulting company, which she just recently launched, is called Curious Evaluation. Avalon provides consulting services for nonprofit organizations to help in evaluating the impact of their programs using data and science by framing the effort around the organization's mission, goals and values.Mentions:Connect with Avalon on LinkedIn Related Links:Curious Evaluation Connect with Us:Chisoo Lyons on LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on: Apple Podcasts, Google Podcasts, Spotify, Stitcher

In this episode, Margot Gerittsen speaks with Kim Grauer. Kim is the Director of Research at Chainalysis, where she examines trends in cryptocurrency economics and crime. Listen as she talks about her obsession with fighting fraud in the cryptocurrency market.Highlights:What is crypto crimeTrust in stable coinMisconceptions around cryptocurrencyUsing data and data science in fighting fraud About the Guest:Kim is the Director of Research at Chainalysis, where she examines trends in cryptocurrency economics and crime. She was trained in economics at the London School of Economics and in politics at Oxford University. Previously, she explored technological advancements in developing countries as an academic research associate at the London School of Economics and was an economics researcher at the New York City Economic Development Corporation. Related Links:ChainalysisNew York City Economic Development Corporation Connect with UsMargot Gerritsen on LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on  Apple Podcasts, Google Podcasts, Spotify, Stitcher

Michelle Katics, CEO  and co-founder of  BankersLab, discusses her journey in risk management training and the importance of integrating technical skills with business and soft skills. She shares her experience in helping banks navigate complex regulations and the need for training to improve understanding and decision-making. Katics emphasizes the importance of storytelling and simplifying complex concepts to effectively communicate with stakeholders. She also highlights the need for women to participate in data science and entrepreneurship, and encourages everyone to continue learning and collaborating to drive innovation and growth. Katics also discusses her involvement in volunteer work, including supporting migrants and refugees and mentoring aspiring entrepreneurs. She concludes by encouraging listeners to embrace diverse skill sets and collaborate to achieve better outcomes.Highlights:Why Michelle went into risk management and why it’s so critical for enterprise success (00:58)Blending business and soft skills with technical skills for optimal outcomes (04:52)Importance of storytelling (07:19)Mentions:Connect with Michelle Katics on LinkedInBios:Michelle Katics is the co-founder and CEO of BankersLab. BankersLab provides a virtual simulation platform taking learning to the next level, combining business expertise in lending with numerical simulation and gamification. Michelle is a thought leader in the fintech revolution and a champion of talent transformation and innovation. During her career she worked at the Federal Reserve Bank of Chicago, the International Monetary Fund, Fair Isaac, and with numerous financial institutions who were her clients in over 30 countries. Alongside her impressive career accomplishments, she has a diverse and rich portfolio of volunteering activities being in service of others.New co-host and the WiDS Chief of Programs, Chisoo Lyons spent years in consulting services, working with clients including leading banks and financial services organizations worldwide. She held several leadership positions in consulting, research, solution development, and business-line management. She kick-started her career as a data analyst at FICO. Today, at WiDS, she remains dedicated to supporting and empowering women in data science.Learn more from data science leaders like Michelle on Using storytelling to communicate with stakeholders.Connect with UsChisoo Lyons on LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts,  Google Podcasts,  Spotify,  Stitcher

In this episode, Mary Krone explores her career shift from a PhD in chemistry and biochemistry to data science, where she builds financial credit models. She highlights her work’s tangible impact and discusses the challenges of work-life balance.Mary’s passion for data science’s positive potential in finance shines through as she debunks misconceptions, talks about career paths, and dives into the evolving world of data science and generative AI.The episode also includes topics of the need for continuous learning and the blend of art and science in data science. Highlights: Mary’s transition from doing theoretical work to work in the real world (00:34)It takes a “village” to be successful (10:06)Managing a team of data scientists and why she describes herself as “leading teams who use data science for good” (21:01)Mary’s views and optimism about the data science field (33:25)Women’s roles in the future of data science (45:07)Mentions:Connect with Mary Krone on LinkedInBios:Mary Krone believes in using data science for good––to make meaningful and positive impact. Currently, she leads a data science team at Credit Karma, a personal finance company. Previously, Mary held various leadership roles in both technical and management tracks at FICO. Mary holds a PhD in Chemistry & Biochemistry from UC Santa Barbara and a BA in Chemistry and Secondary Education from Vassar College.New co-host and the WiDS Chief of Programs, Chisoo Lyons spent years in consulting services, working with clients including leading banks and financial services organizations worldwide. She held several leadership positions in consulting, research, solution development, and business-line management. She kick-started her career as a data analyst at FICO. Today, at WiDS, she remains dedicated to supporting and empowering women in data science.Learn more from data science leaders like Mary on Data Science Leadership: Creating Meaningful Impact.Connect with UsChisoo Lyons on LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Kate Kolich serves as the Assistant Governor and the General Manager of Information Data and Analytics at the Reserve Bank of New Zealand. With an extensive background in the financial sector, she also has significant public sector experience. Throughout her impressive career, she's delved into areas like data analytics, digital strategy, information management, data governance, business intelligence, and data warehousing, among others. Soon after the launch of Women in Data Science (WiDS) at Stanford, Kate became an active WiDS ambassador. She has organized numerous WiDS conferences in New Zealand, spotlighting nearly 100 female data scientists. Beyond this, Kate is a passionate mentor and supporter of many professionals in New Zealand. In this episode, we discuss Kate's role at the Reserve Bank, the role of her team, highlights from her career, and her insights on being a successful woman leader in her field.For Detailed Show Notes visit our website.In This Episode We Discuss:Kate’s role at the Reserve Bank of New Zealand.Data Guardianship: the concept of ‘kaitiakitanga’(guardianship in Te re Māori) and its relevance for those working with data.Kate’s evolution from a hands-on tech role to impactful leadership.How Kate overcame self-doubt early on in her career.Championing innovative data visualizations at the EECA to create greater impact.The value Kate places on mentorship and helping others grow in their careers.Kate’s association with WiDS New Zealand: Organizing conferences and spotlighting female data scientists.Kate's journey of realizing the significance of leadership and communication for broader impact.RELATED LINKSConnect with Kate Kolich on LinkedInFind out more about the Reserve Bank of New ZealandView the EECA’s New Zealand Energy Scenarios Data Visualization View the data and statistics published by Kate’s team at RBNZ Statistics - Reserve Bank of New Zealand - Te Pūtea Matua (rbnz.govt.nz)Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide) Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Telle Whitney began her career in the tech industry in 1986 after earning a Ph.D. in computer science from Cal Tech. Her journey into graduate studies was sparked by an encounter with graphics during her undergraduate studies at the University of Utah. Although she initially wasn't interested in graphics, the idea of computer-aided design fascinated her, and she was drawn to work with Ivan Sutherland, a co-founder of the computer science department at Cal Tech.Throughout college, Telle learned various programming languages, starting with C as an undergraduate and later delving into object-oriented languages like Simula and Mainsail. While she hasn't programmed in years, Telle acknowledges that programming languages evolve and change rapidly, but once you understand the core concepts, transitioning to a new language becomes relatively easy.Reflecting on her path into computer science, Telle admits that she had no exposure to the field during high school, which is a common experience for many young girls. “It wasn't until my sophomore year, where I was at my wit's end of trying to figure out what to study, and I took this interest test that compared your interests to other people's interests and programming came out on top.”From her first programming class, Telle knew she had found her calling, even though she started later than many of her peers. Telle's love for programming stems from its logical nature. “When you’re writing a program, and you’re trying to solve this problem, it is so absorbing. I would become completely captured with whatever I was working on at the time, and it was very fulfilling, no question.”She advises aspiring coders to ignore the myth of natural ability in programming and the notion that girls are not good at math. Persistence and patience are key in navigating the challenges that arise, and the belief in one's ability to succeed is crucial.Discussing the persistent stereotypes and biases that deter women and people of color from pursuing careers in tech, Telle, and Margot highlight the prevalence of these harmful beliefs even today. Despite efforts to increase diversity, Telle emphasizes that more needs to be done to ensure the best minds participate in shaping the future of technology. Both Telle and Margot stress the significance of representation, with Margot outlining the WiDS goal of achieving at least 30% female representation by 2030, given that the current representation stands at a mere 10%. Such representation can help drive a cultural shift and improve the treatment of underrepresented groups.Telle dedicated 20 years to working full-time in the chip industry, actively striving to bring about change within the field. Concurrently, she collaborated with her close friend Anita Borg on the Grace Hopper Celebration, an initiative aimed at celebrating women who create technology. When Anita fell ill with brain cancer, Telle was asked to step into the role of CEO. During her 15-year tenure, Telle successfully expanded Anita Borg into a prominent organization.Although she hadn't planned to take on this role initially, Telle saw it as a valuable opportunity and made a conscious pivot. She has since left Anita Borg to establish her own consulting firm, proud of the impact she made and the organization's continued influence under new leadership.The lack of progress in achieving diversity in the tech industry is a cause of concern for Telle. Breaking down barriers and changing the perception of what a technologist looks like remains an ongoing challenge.Telle's particular interest lies in fostering a more inclusive culture within organizations. While community plays a vital role, Telle believes that actual cultural change stems from providing equal opportunities for advancement.Offering advice to aspiring data scientists, Telle urges them to take risks, develop confidence in their ideas, and master effective communication. She emphasizes the importance of curiosity and creativity in shaping the future and encourages aspiring data scientists to be at the forefront of technological advancements. “I want you to be at the table creating a technology that’s going to change our lives. That’s what you should do.” RELATED LINKSConnect with Telle Whitney on LinkedInFind out more about AnitaB.orgConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)​Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Srujana Kaddevarmuth began her career near Bangalore, India after completing her master’s degree in engineering from Visvesvaraya Technological University. She has had a successful career in the tech industry and currently holds the position of senior director at Walmart's Data and Machine Learning Center of Excellence.In her role as senior director at Walmart, Srujana leads the AI portfolio for various aspects of the company's retail business, including omni retail, new and emerging businesses in the consumer and tech space, data monetization, and membership. Her primary responsibility is to drive innovation and promote the democratization of data and AI, aiming to create value for consumers, associates, and the business as a whole.Despite coming from an academic family, Srujana chose to pursue a career in the corporate sector rather than academia. After obtaining her bachelor’s degree in engineering, she gained real-world exposure to data science and AI while working at the Energy and Resources Institute. This experience fascinated her, leading her to pursue a master’s degree in engineering with an emphasis on operational research and data science.She then started her career as a data scientist at Hewlett-Packard, where she worked on market mix models in the consumer and marketing domain. Later, she led the big data analytics center of excellence at Hewlett-Packard and went on to work at Accenture, where she led a partnership with Google, developing various models for consumer hardware products before joining Walmart.Entering the corporate world after graduation, Srujana was surprised by the importance of collaboration in data science. She realized that building excellent algorithms alone is not enough; teamwork and collaboration are essential, particularly in applied data science.As a leader, Srujana prioritizes assigning projects to data scientists and AI experts based on their individual interests to keep them intellectually stimulated. She also empowers her team to make informed decisions based on available data. Her team is trained to use AI responsibly, with a focus on explainability, transparency, fairness, and bias elimination.With the increasing delegation of decision-making to algorithms, from trivial choices to significant ones in immigration systems, legal sentencing, and healthcare, it becomes crucial to protect consumer privacy and eliminate unintended consequences. Srujana explains that responsible generation and consumption of algorithms and data are paramount.One of Srujana's major challenges lies in creating proofs-of-concept that effectively translate into tech products and developing unbiased algorithms. “When we deploy these machine learning algorithms, many people fail to understand that these algorithms are the statistical representation of the world that we live in, and they may not necessarily be perfect and interpretable at times, as we have seen certain racist comments unleash on social media sites.” Addressing these issues, according to Srujana, requires eliminating signals of bias through careful data curation and training algorithms to avoid institutionalizing bias associated with certain data sets.Srujana is excited about the diverse advancements in data science, particularly in space exploration, healthcare, and agriculture. In addition to her work with Walmart, Srujana serves on the board of the United Nations Association, San Francisco chapter, where she utilizes data science to drive meaningful decision-making for the protection of our ecosystem.When asked what advice she would give her 18-year-old self, she responds that she would encourage herself to be open to the emerging field of data science and embrace its opportunities. Her advice for other data science enthusiasts is similar: “We have just started to open some new realms in the domain of data science and AI with generative algorithms as well as quantum computing, so I would just urge data science enthusiasts to be open to where this domain takes them.” RELATED LINKSConnect with Srujana Kaddevarmuth on LinkedInFind out more aboutWalmartLearn more about the United Nations Association San Francisco ChapterConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)​Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Today Veronica Edwards is a senior data analyst at Polygence, though her educational and career background encompasses a wide range – she has delved into everything from dance and choreography to physics, sociology, marketing, and most recently, data science. Polygence is a nonprofit that offers middle and high school students a 10-week research experience under the guidance of a professional mentor. As a senior data analyst at Polygence, Veronica uses data to help build and scale the company and to provide students and mentors with an optimal experience.Upon working at Polygence, Veronica was surprised to learn how little high school students are asked to do independent research. Independent research affords students the opportunity to explore their passions, get comfortable with the ambiguity of the research process, and become experts on their chosen topic. Polygence aims to democratize this research experience and has successfully targeted a diverse selection of program participants, attracting mentors and students in over 100 countries with a near-equal split of female and male participants. Growing up, Veronica trained vigorously as a ballet dancer alongside peers who aspired to be professional dancers, though she knew early on that she did not want to pursue a career in dance. Veronica believes her training as a dancer helped her build strength and perseverance that have served her throughout her career. Furthermore, the creativity she uses for dance and choreography informs her work as a data analyst, helping her to tell the story of the data she oversees.Veronica entered Princeton University as a physics major and then transitioned into sociology, where she saw how data could be used to understand society. While attending college, she explored different career paths through Princeton’s connections with the public sector. This led her to multiple internships in public service, including a marketing internship at Community Access, an NYC-based nonprofit. Upon graduation, she was accepted into a Princeton P-55 Fellowship, which connected her with her first job out of college as an executive assistant at ReadWorks, a nonprofit that helps K-12 students with reading comprehension. Veronica recalls a clear moment at ReadWorks that propelled her into data science. “The senior engineer was in the office one day and he asked me, ‘Veronica, do you want to learn how to pull data on your own?’ In that moment I didn’t know what SQL was, I had never heard [of] it before, but I said yes.”Veronica sees her non-technical background as an asset in data science because it allows her to think like other people, particularly those without technical backgrounds. “I come from a non-technical background, and so therefore for me, I'm a step ahead of people who do have a technical background, in explaining data because I know what it's like to not understand what's going on in a chart, for example, or what a P-value is.”When asked what advice she would give to herself 10 years ago, she says she would tell her not to write off subjects that she enjoys but isn’t the best at. “I was always decent at math and decent at statistics and pretty good at all of these subject matters, but I wasn’t the best. If I would have told myself back then [that] one day you’re going to have a career in data science, I would’ve been really intimidated, because that seems like something you need to have extremely high standards for.” Additionally, she would urge her younger self to be open-minded about her future plans, because in her words, “you never know what opportunities are going to present themselves.”RELATED LINKSConnect with Veronica Edwards on LinkedInFind out more about PolygenceConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFollow WiDS on Twitter (@WiDS_Worldwide), Facebook (WiDSWorldwide), and Instagram (wids_worldwide)​Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

As Estée Lauder’s first-ever Chief Data Officer, Jane Lauder is combining data science with creativity to fuel the growth of the company. Jane has worked at Estée Lauder for 26 years – 24 of which she spent working on the brand and marketing aspects of the business. While working as the Global Brand President of Clinique, Jane saw the power of data to drive all aspects of the business, motivating her to transition into her current role. Estée Lauder Companies is one of the world’s leading manufacturers and marketers of luxury skincare, makeup, fragrance, and hair care products. It encompasses a house of about 30 brands including their flagship brand Estée Lauder, as well as other brands such as Clinique, Crème de la Mer, MAC, Jo Malone, Aveda, Le Labo, Bobbi Brown, Origins, Dr.Jart+, Too Faced cosmetics, and more. The beauty giant was founded by Jane’s grandmother, Estée Lauder, over 75 years ago. Before there were data and analytics to pull from, Estée Lauder would gather information about potential consumers by analyzing women’s bathrooms, paying close attention to details such as décor and the colors of their tiles. She then used this information to design aspirational product packaging that would elevate its surroundings. “In the beginning, we were a one-woman research company, and that one woman was Estée Lauder.”Today the company has a wealth of digital and in-store data. Jane and her team use this data to understand consumers’ aspirations better, gain insight into how different consumers use their products, and spot emerging trends in the cosmetic industry. This information helps them to respond to trends and tailor their products and messaging to meet consumers' unique needs and aspirations. As Estée Lauder’s Chief Data Officer, Jane’s biggest obstacle resides in deciding how to best utilize the ample data she has access to. Another obstacle lies in determining how to strike a balance between satisfying consumer needs today and investing in the future of the company. “You want to be able to use the data you have to create incredible opportunities, but also think about how to unlock the data for the future, and how to set up the foundational data sets, and data containers, if you will, to be able to create this quick analysis of the future.” Jane believes the future is promising for those seeking roles as data scientists within the cosmetics industry. The cosmetics industry is teeming with opportunities to connect with consumers and make a difference. 

An expert in climate change and the optimization of power grids, Priya Donti researches how to use machine learning for forecasting, optimization, and control of power grids to facilitate the integration of renewable energy. She first became interested in climate change during high school and studied computer science with a focus on environmental analysis as an undergraduate at Harvey Mudd College. After graduation, she spent a year on a Watson Fellowship, learning about different approaches for next-generation power grids in Germany, India, South Korea, Chile, and Japan. She went on to earn her PhD in power grid optimization at Carnegie Mellon. While there, she co-founded Climate Change AI, an initiative born out of a paper she co-wrote with academic and industry leaders about the ways machine learning could address climate change.Machine learning can play a role in mitigating climate change in areas like decarbonizing power grids, buildings, and transportation; helping create more precise forecasts for climate change impacts; and strengthening social, food, and health systems to cope with the impacts of climate change. There are several ways to apply machine learning to the climate crisis. One is distilling raw data into actionable insights, like turning satellite imagery into inputs on where the solar panels are or where deforestation might be happening, or turning large amounts of text documents into insights to guide policy or innovation. A second way is forecasting solar and wind power, and extreme weather events. A third is optimizing complex systems to make them more efficient, like heating and cooling systems in buildings or optimizing freight transportation systems. Machine learning is also valuable in science and engineering workflows to accelerate the design of new batteries or speed up climate or power models.While there are many ways that AI and data science can play a role in climate action, sometimes it’s difficult figuring out where to start. Priya says the WiDS Datathon is a great way to get started because no matter how much experience you have, you can enter and be able to work on this particular challenge. “The floor is low, but the ceiling in high.” There are also many resources on the Climate Change AI website to start learning, get involved, and meet other people working in the space through workshops, virtual happy hours, mentorship programs, and an online community platform. RELATED LINKSConnect with Priya on LinkedINFind out more about the Climate Change AIConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Lesly Zerna earned her undergraduate degree in Telecommunications Engineering at the Bolivian Catholic University and then traveled to Brussels to complete a Masters in Computer Science. After returning to Latin America, she began teaching data science and AI both in universities and virtual platforms and today her courses have thousands of online students. She brings insights from her experiences working in large companies overseas to her students in Latin America. For those just starting in data science, she says you must first identify your personal learning style (e.g., visual or text) to improve your learning experience and start with a general overview of the field. Next, find a practical topic you’re interested in, and look for projects, examples, authors, researchers who are working in that area. Do all of this while continuing to develop the fundamental skills you need (e.g., languages, platforms, frameworks) in data science. Lesly transmits her passion for learning to her students by using real scenarios instead of theory in textbooks. She lets them experience what works, shows the development process, and where common mistakes are made. She says it’s important for students to find where the problem is, know how to solve it, and make decisions. She believes there’s a lot to learn from the world of entrepreneurshipas you not only develop a project, you also have to develop the skills to explain and present the project, sell it, and negotiate. She believes that mentoring is essential to break down barriers for women. It can help dispel myths and biases about women in science and technology jobs, and learn from successful women that in spite of a hard path, they were able to achieve and follow their dreams.RELATED LINKSConnect with Lesly on LinkedINFind out more about the Universidad Privada BolivianaConnect with Cindy Orozco Bohorquez on LinkedIN

Leda Braga is the founder and CEO of Systematica Investments, a hedge fund that uses data science-driven models to support its investment strategies. Leda was born and raised in Brazil and found her way into the financial sector after getting her PhD in engineering and spending several years as an academic. Her financial career started with seven years in investment banking at JP Morgan and then she joined the hedge fund startup BlueCrest in 2000. She explains that while her funds did very well during the 2008 financial crisis, the time felt like an existential crisis because you didn’t know if the major investment banks were going to survive. But she said it was a formative time and she learned many lessons. Several years after the financial crisis, she spun off her own firm, Systematica Investments focused on systematic trading.Leda explains that systematic investment management is data science applied to investment. The systematic approach makes the investment process less reliant on the random nature of forecasting and more reliant on risk control in portfolio construction.Both discretionary traders and systematic traders are looking at information to try to make decisions. Those who do it on a discretionary basis tends to look at the data and make a decision to make money on a trade. Those that look at data on a systematic basis build data-driven processes for trading strategies for certain risk profiles and preferences that will produce consistent returns over time. She says the responsibility weighs heavily on her to ensure a good return because people's pensions are part of the money her firm manages.While she believes strongly in the power of leveraging data science in investment, we’re not yet at a point where AI allows us to do “autonomous investing” because there's a large element of randomness in markets and relatively sparse data so learning algorithms have limited use. She says that the only way it might be possible is if you’ve compartmentalized and narrowed the scope to the extent that you have a controlled amount of randomness. Learn more about Leda and systematic investing in her 2018 WIDS presentation, When Data Science is the Business.RELATED LINKSConnect with Leda on LinkedIn or TwitterFind out more about Systematica InvestmentsConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile​

A Colombian engineer, Jessica is fascinated by the processes and complexity of water supply systems in urban areas.In her post doc research in Australia, she brings together her expertise on the water hammer and transient flow waves to create an AI model that is able to identify where pipeline defects are faster and more accurately than existing techniques.She explains that in data science, the most important stage is understanding the problem. You need to bring in basic knowledge of the problem and expertise from other disciplines that are involved in a problem and combine that with artificial intelligence. AI is an important tool but just part of the solution. It’s critical to maintain all the legacy of knowledge and understanding of a problem. AI can make it simpler to apply, but you can’t leave behind the physics or knowledge of the hydraulic part of water movement. Working in industry, she has found that it’s important to first understand how the system works. In these large companies in charge of delivering water, each person has different objectives, so you need to understand how the company works, who is in charge, what are their objectives, and how they measure their success. If your research project aims at those things, they will be more receptive and a better chance of success.Jessica has learned in both research and industry consulting that nothing works the first time and it’s important to not to let those little defeats build up in your head. You need to trust yourself. There are many moments in life when you are criticizing yourself, and you realize that the biggest enemy you have is yourself. She just breaks down the problem into small parts and then solves each part one by one. She is passionate about teaching and inspiring young engineers about the importance of water and the future of this invaluable resource.RELATED LINKSConnect with Jessica on LinkedINFind out more about the University of AdelaideConnect with Cindy Orozco Bohorquez on LinkedIN

As a quantitative social psychologist, Karolina has always been interested in using data to measure human behavior to try to understand it better. She has researched questions around political attitudes and polarization, particularly in light of Brexit and Trump’s election in 2016. She wanted to understand how people could arrive at completely different understandings of the world and reflect it in their voting decisions. One of her findings was that in the American two-party political system, people tend to identify as either Republican or Democrat and are more likely to agree with statements from their identified party. People use identity cues as mental shortcuts to judge information because there’s simply too much information to decipher. She says the polarization is stronger in the US where there are just two major parties compared to other countries with more choice of multiple political parties.After her undergraduate and Ph.D. in psychology and two post-doctoral positions, Karolina decided to leave academia and to work for the nonprofit Teach First. She felt there was a lot of pressure in academia to become an expert in one niche and she wanted the freedom to pursue multiple topics that interest her. When she landed her first job outside of academia, she said the adjustment was a bit challenging, for example, when she first got the data to work with. In academia she knew exactly what the labels were, but in a new organization, she had to figure out how they measure things, what information they store, or what they use as a proxy for a certain behavior. As a researcher at Teach First, a non-profit in the UK that trains early career teachers to work with schools in disadvantaged areas, she is currently evaluating the impact of their programs in schools across the UK. She wants to know if their programs actually have an effect on the pupils that are being taught by their teachers compared to others.When reflecting on her career, she says there have been times when she questioned whether she had the right skills. She has learned that it’s OK to be uncomfortable in a new position. With any new challenge you take, it takes time to get to know that new environment, and get to a place where you can start confidently contributing. It’s part of growing and learning, the satisfaction that you get from crossing that bridge from being very unsure to getting to place where you're comfortable and succeeding is very rewarding. The process of maturing in your career is accepting that this is just going to be part of the journey.RELATED LINKSConnect with Karolina on LinkedIn or TwitterFind out more about Teach FirstConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

EPISODE NOTESWiDS Executive Director Margot Gerritsen welcomes her new co-host, Cindy Orozco, in a wide-ranging conversation about their career paths and valuable learnings along the way. Cindy is thrilled to be joining as podcast co-host and believes that showcasing women at all stages of their careers shows that we “share the same fears or experiences every day. It's just that some of us have been on the path a little bit longer than others.” Cindy is an applied mathematician who is currently working as a machine learning solutions engineer at Cerebras Systems. Originally from Colombia, she loved applied math, and did a master's in civil engineering and mathematics from King Abdullah University of Science and Technology (KAUST), in Saudi Arabia, and a PhD in Computational and Mathematical Engineering from ICME at Stanford. She met Margot at Stanford and has been contributing to WiDS for many years at conferences, workshops and datathons.After answering some questions about herself, Cindy stepped right into her co-host role to interview Margot. A native of the Netherlands, Margot said her career path was similar to Cindy’s as she started in math, got excited about applied math, and decided to study fluid mechanics. After getting her PhD at Stanford, she became a professor at the University of Auckland in New Zealand and then returned to Stanford where she has been a professor for 20 years. During this time, she has been an accomplished researcher, professor, mentor, and leader in the School of Earth, Energy & Environmental Sciences, the Institute for Computational & Mathematical Engineering (ICME), and Women in Data Science (WiDS).When asked how she managed to juggle all of these things, Margot said she learned to not worry about making mistakes or striving for perfection, saying, “80% is perfect”, adding “I always felt I can't have it all. So you make choices, and there's always something that's got to give.” Cindy agreed that the busier she is, the better she manages her time, and when you have many balls in the air, often what you learn in one area can help you solve problems in another. In discussing the “imposter syndrome”, Margot said she had often felt like an imposter, and soon discovered this was a common feeling among students and faculty at Stanford. And it’s even stronger when you stand out, like a woman in STEM. It puts an extra burden on you to succeed to set the example for those who come after you. The pace of research in AI and deep learning contributes to feeling like an imposter. People publish very quickly and it's hard to understand what really good solid research is and what is just an idea. It gives people this sense that they're not on top. They forget the purpose of school is creating a lifelong interest in learning. “There's a lot of failure on the way to success. My favorite definition of an expert is somebody who's made every possible mistake.”RELATED LINKSConnect with Cindy Orozco on LinkedIN Find out more about Cerebras SystemsConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

When Tahu Kukutai’s father went to school, he wasn’t allowed to speak his native language. If he did, he would be hit by his teachers. While the situation for the Maori people in Aotearoa (the Maori name for New Zealand) has improved somewhat since then, Tahu has dedicated her career to advocating for the rights of Indigenous peoples to preserve their native language, identity, communities, and culture. In today’s world, power over data is a central component of indigenous self-determination. Government agencies decide what data gets collected on whom and how it gets used, shared, and stored. There's a long history of data colonialism and state surveillance of indigenous communities designed for government priorities. Indigenous data sovereignty provides a framework to determine what data are collected and how it’s used, the ethical framework and governance, and the intended beneficiaries. Indigenous peoples have a different perspective on data. Most western frameworks about data protection, rights, and privacy are focused on the individual. The indigenous concept of data, data sovereignty and data rights are instead focused on the collective. Indigenous data are any information that impacts both the individual and the collective such as natural resources, nations, peoples, traditional and cultural information, oral histories, and ancestral knowledge. Tahu is advocating for indigenous data sovereignty as a founding member of the Māori Data Sovereignty Network Te Mana Raraunga and the Global Indigenous Data Alliance (GIDA) that works to control the collection, ownership, and application of data about their people, territories, and natural resources.While there's a growing willingness from government agencies to engage in conversations around Maori data sovereignty, she says there is a lot of work ahead. Private sector big data organizations also have a major role to play, and she hopes they will be more proactive about doing this work internally to be relevant to the growing Maori population. Maori currently make up 16-18% of the Aotearoa population, compared to roughly 5% indigenous populations in the US, Canada, and Australia. Tahu says that advocating for any issue fundamental to the wellbeing of the Maori, whether it’s language revitalization, land rights, or water rights, has always been driven by the Maori. “I have huge optimism that change can occur, but it will only ever be driven by ourselves. That doesn't mean that others don't have a role to play and there are some very positive steps,” she says. “There's incredible resilience, fortitude, and tenacity in indigenous communities around the world who have refused to let go of their identity. The challenge is to move from surviving to thriving.”RELATED LINKSConnect with Tahu on LinkedIN and TwitterFind out more about National Institute of Demographic and Economic Analysis (NIDEA)Find out more about University of Waikato Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Allison Koenecke, who received her PhD from Stanford’s Institute for Computational and Mathematical Engineering (ICME), describes how her experiences in academia and industry shaped her decision to return to academia. Currently a postdoc at Microsoft Research in the Machine Learning and Statistics group, she starts as an Assistant Professor of Information Science at Cornell University next year. Her research interests lie at the intersection of economics and computer science, with projects focusing on fairness in algorithmic systems and causal inference in public health.​Allison says in her career so far, she has always tried to keep as many doors open as possible but recognized, at some point, you have to start closing doors and specialize. After getting her bachelor’s degree in mathematics from MIT, she worked in economic consulting for a few years and realized she wanted to do something with more social benefit. While she was working in industry and during summer internships, she kept in touch with professors and kept up with her research so she could have that option open if she wanted to go back to school. One of the main reasons she chose to stay in academia was industry and government did not offer what she was looking for. For example, if you stay in industry long-term and your research is critiquing big tech companies, you may run into roadblocks or not be seen as a neutral third-party observer, as you would be seen in academia. Or at a government think tank, your work wouldn't necessarily have as much reach as in academia. But even more, a lot of the reason she stayed in academia was the people. Allison’s research is interdisciplinary and falls into two categories. The first is a fairness in online services and algorithmic services, such as speech-to-text or online ads and looking at the racial disparities in those services. And the second branch is on causal inference, which is usually applied to things like public health. Most of her thesis focuses on fairness with the services that we use every day.One of her research projects is about Google ads used to enroll people in food stamps and how to make decisions about fairness when it costs more to show those ads to Spanish speakers versus English speakers. She is also doing fairness research on racial disparities on speech-to-text systems developed by large tech companies to ensure systems are usable for African American populations that may not able to use their tools simply because they speak with a different variety of English than standard English. She says you need to have people thinking about fairness problems at all steps of the pipeline before you build a product that might harm certain groups of people. She’s hoping to bring awareness to different blind spots to make sure technology actually works for everyone.RELATED LINKSConnect with Allison on LinkedIN and TwitterFind out more about the Microsoft Research Machine Learning and Statistics groupFind out more about Cornell University Information ScienceConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Though Karina showed an early aptitude in math, her high school counselor advised her against pursuing an engineering degree. She ignored his advice and went on to earn her undergrad degree in mechanical engineering from the University of Rhode Island and a PhD in Aeronautics from Caltech. She landed her first job as a speech-to-text engineer at TRW where she was awarded her first patent. She then moved on to technology transfer as a patent agent at the Jet Propulsion Lab. She bounced back to academia, managing corporate partnerships for Caltech, and then returned to industry as Google’s University Lead for Google Cloud AI/Machine Learning. She is now at SAP as Global Head of Academies and University Alliances, continuing to connect industry and academia.In her diverse career spanning business and education, she has seen increasing power concentrated in big tech companies through their ownership of immense datasets and computational power. Companies are also attracting talent away from universities that are now having a hard time hiring enough computer science faculty. She says there are some creative ways to bring back some balance by companies hosting visiting faculty and industry partners coming in to teach at universities.Karina is also very concerned about ensuring fairness in data science. She explains that it’s not just the software that's being developed, but the datasets that are used to create predictive models. If a company just collects data from one demographic and then applies it to everyone, that introduces bias, and then the algorithms amplify these biases. She believes that the only way to address this is to have more ethnic, gender and geographic diversity in the field of data science. She sees a vital need to encourage more women and minorities to enter the field to bring diverse perspectives to data science.For people interested in pursuing a career in data science, she advises gaining the basic skills in math, science, and programming languages, but the most important quality is the ability to learn because everything is constantly changing. She recommends keeping your options open, acquiring as many skills as possible, and sharpening your interpersonal skills. Karina also says to challenge yourself. “We don't grow in a space of comfort. You grow when you're challenged, it's okay to be uncomfortable because that's likely the place of greatest growth. There’s no such thing as failure, you either win or you learn.”  RELATED LINKSConnect with Karina on LinkedIn and TwitterFind out more about SAPConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Fatima Abu Salem grew up in Lebanon and has focused her data science research on addressing critical challenges in the region, including problems around the Syrian refugee crisis, the water quality in Lebanon and their irrigation requirements for farmers. Fatima explains that her life journey is really enshrined in the conflict of the region. She was born to a Lebanese mother and a Palestinian father who had refugee status so she inherited this status. This meant there were many restrictions on what career she could pursue and her dream to become a doctor was out of reach. But she was good in math so majored in mathematics at the American University of Beirut and one of the only jobs available to her as a Palestinian was able to become a high school teacher. She taught for a few years but realized it wasn’t what she wanted to do with her life. At the time, she was working on her master’s thesis in Algorithmic Number Theory at the American University in Beirut and decided she wanted to pursue a PhD. She emailed Oxford’s Computing Lab and a prominent professor there, Richard Brent, wrote back and invited her come study there. While at Oxford, she studied computer science, building on her knowledge of mathematics. After Oxford, she returned to AUB in Lebanon in the fall of 2004 as a faculty member.Data science was gathering momentum, so she started learning about it and was especially excited about applying data science for the public good. Fatima is currently using data science to help out with some of the most urgent and critical questions in Lebanon and in the Middle East. She focused on what mattered to her most about the livelihood of people such as the plight of refugees, fake news, the demand on the power grid and infrastructure, food security and healthcare. She wanted to help empower the Lebanese institutions to learn how to make better predictions so they can be more prepared than when they were taken by surprise by a million and a half refugees coming to Lebanon.She has built collaborations with experts from the medical, agriculture, and public health schools for different research projects for social impact. One is trying to predict an irrigation metric to help farmers optimize their water usage so they can increase crop yields and reduce power consumption. Another project is trying to predict dementia with a public health school and a third project trying to predict birth defects using data using air pollution data.In Fatima’s talk at the 2021 WiDS Conference, Doing Data Science in Data Deserts, she discusses these projects and others such as quantifying anti-refugee bias across Lebanese news corporations, fake news detection, predicting primary health care demand by Syrian refugees in Lebanon, and understanding Syrian refugee mobility in Turkey. RELATED LINKSConnect with Fatima on LinkedIn and TwitterFind out more about American University of Beirut (AUB)Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Louvere Walker-Hannon has worked at MathWorks (the company that makes MATLAB) for over 21 years, where she’s also a STEM Ambassador. She studied biomedical engineering as an undergraduate at Boston University and did graduate work at Northeastern University in geographic information technology with a specialization in remote sensing.She loved working with MATLAB as an undergraduate and when MathWorks came to the career fair when she graduated, she sought them out, got an interview, and has been working there ever since.She says there are both technical and non-technical skills that are valuable in the field of data science. Technical skills include coding, some programming, a foundation in mathematics, some statistics, and in some cases physics. Non-technical skills are also very important. It’s critical to be able to communicate your findings clearly using a variety of techniques. She says stay away from technical jargon and communicate as if you’re having a conversation. A second important skill is active listening, to be open to suggestions from others, especially those who are new to the field.She explains that there are also barriers to people entering the field of data science. For some people, coding is a barrier to engagement with data science as many people in STEM professions are not comfortable with coding. MathWorks is doing more development to provide user interfaces or apps to give people a starting point without having to rely on writing code.There are also concerns about model interpretability where it’s difficult to get insights into how certain models work. More people are gaining awareness about the topic, and that's leading them to explore how to implement it and ask why it’s important. She is noticing that more people are trying to incorporate model interpretability into their data science applications.One of the systemic barriers is implicit bias. People are used to working with and being around people with certain characteristics. And in a work setting, there could be a project coming up, and there are several individuals who could work on this project. Many times, the people selected to work on a project tend to be the same individuals. But then it begs the question, when do others get the chance? There’s still a lot of under-representation from various population groups in data science. Even if people from an under-represented group have the skills and education, if they don’t feel like they belong, that can impact their productivity. It’s important to build a sense of community and have someone guide the person, make them feel welcome and help them become a part of the culture, so they can understand what they can do in order to thrive. Louvere is also a STEM ambassador at MathWorks where she volunteers in STEM advocacy and outreach in schools or on STEM panels. She loves hearing high school age and younger students at science fairs talk about their projects and see how proud they are of their work. This gives her hope that young people are excited about data, about analysis, and communicating their insights to others. RELATED LINKSConnect with Louvere on LinkedIn and TwitterFind out more about MathworksConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

After she earned her BA, MA, and PhD in economics from the University of Maryland, Menglin Cao spent six years at Fannie Mae before joining Wells Fargo. Over the past 15 years, she has seen a major shift in how financial institutions use data to drive business decisions. In the past, many business decisions were based upon the experience and judgement of senior executives, but today every decision must be backed up by data and analytics. Many aspects of the financial services that leverage data are great candidates for AI and ML models, such as customer experience, revenue generation, and risk management. She wants to ensure they build models that are fair, explainable, interpretable, and able to generate value.She offers several examples about best practices for success.To be useful, the data must first be focused on a foundational business question. “Data can tell many stories, but until you bring the story to the businesses and they can relate the story to a question, experience or concern that they have, it’s not going to lead to an actionable insight.” The journey to find that answer requires data science, data algorithms, operational procedures, implementations, and integrations of systems, but without the right question to begin with, it doesn’t lead anywhere. It’s also important to build a solution in a matrix organization rather than top-down approach, working with the technology team, model developers, subject matter experts, the model governance and validation teams. And data management has become centralized, shifting from silos to a central enterprise data lake where every line of business and all models depend on one source of truth. This removes the redundancies and duplications that silo-based data warehouse may create.To ensure that models are fair, they continually test them for bias. One way to make sure that these data are representative of the entire population is to conduct a sensitivity analysis. They can see if the model can stand on its own by “shocking it” in different ways to find any vulnerabilities that need to be addressed.She acknowledges that there are not yet many women leaders in fintech and the financial sector but she is hopeful that can change if women are given the opportunity. “I am in the position that I am in because my leaders trusted me and gave me a chance.” She hopes that leaders will continue to give diverse people a chance to grow and demonstrate what they can do. And then once the opportunity's given to you, then the rest depends on what you make out of it. RELATED LINKSConnect with Menglin Cao on LinkedINConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Karen Hao trained as a mechanical engineer and then joined a Silicon Valley startup, thinking that technology was the best means to create social change. While surrounded by smart people who were also passionate about using technology for social change, she soon discovered there were no incentives or pathways to accomplish this. “When you're inside a technology company and you're thinking this is going to help change the world, you're often blind to unattended consequences of your work,” she says.She decided to transition to a career in journalism where she could create social change by raising awareness about social impacts of technologies like AI, and how big tech companies engage in “ethics washing” to protect their profits. She is intrigued by the way that incentives shape the work that is done at a systemic level. She says every tech giant suffers from issues at the systemic level where there are people who deeply care about ethics within the organization, but it doesn't mean the company is willing to change the way their profitable technologies work. And employees are disincentivized to do this work because they could be fired.A high-profile example was the ethical AI team at Google was doing great work critiquing some of Google’s practices and tried to get the paper published. Google refused to let them publish it, censored their research and then fired both of the team leads. It later came out that this was just one instance of academic censorship, but Google had told many other researchers to strike a positive tone when talking about technology being developed by Google.For her article, How Facebook Got Addicted to Spreading Misinformation, she did a nine-month investigation into Facebook's responsible AI team that was supposed to be understanding and mitigating the unintended consequences of Facebook's algorithms. She found the team was focused on specific unintended consequences that are good for Facebook's growth like AI bias. It completely ignored the most important harms of Facebook's algorithms—misinformation, amplification, polarization, exacerbation, especially in the wake of the January 6 capital riots—because addressing them would undermine Facebook's growth. There have been times when Facebook was not only ignoring or negligent of the issues that its algorithms might be causing, but also purposely undermining some of the efforts to try and fix it because of this tension with the company's growth.She was glad to see policymakers cite her article at a recent congressional hearing, and hopes Congress has the political will to regulate companies like Facebook. She says it’s also important for every new generation of Facebook employees to become educated about these issues so they will hold the company accountable. She thinks AI research has shifted a bit over the last five years to be more focused on taking responsibility for societal impacts and part of that evolution is being driven by people on the inside who raised awareness and advocated for change. One of Karen’s inspirations to go into journalism was Rachel Carson's book Silent Spring that sparked a widespread environmental movement. She strives to write stories that activate that same level of change, transforming both the cultural discussions and policy around important issues.RELATED LINKSConnect with Karen Hao on LinkedIN and TwitterFind out more about Karen on her websiteFind out more about MIT Technology ReviewConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

The multi-talented Cecilia Aragon is a data scientist, professor, author and champion aerobatic pilot. In this podcast, she explains how learning to fly gave her the confidence to pursue her career in human-centered data science and as an author.Her book, Flying Free: My Victory Over Fear to Become the First Latina Pilot on the US Aerobatic Team, is the story of how a timid daughter of immigrants who had terrible phobias overcame her fears to become a champion pilot. Learning to fly and excelling at it helped her overcome emotional barriers from childhood when she was fearful and doubted her abilities.In her mid-20s, she was pursuing her PhD and felt a lack of confidence, so dropped out of the program. “It wasn't that I had failed life, but I was living a very narrow life. I was just saying no to everything that might be exciting or interesting. And I saw my life stretching in front of me as incredibly narrow. A colleague offered me a ride in a small airplane…It suddenly occurred to me that living life too safely was dangerous for my spirit.” So, she took her first ride in the small plane and knew that she was going to learn how to fly.She was very fearful as she was learning to fly and realized it was the same feeling she had in grad school when she didn't think she knew enough to be there. She practiced a discipline of constant learning, trying, making mistakes, relearning, and trying again until you get it right. She builds in safety protocols to anticipate potential problems, and most of all, never gives up.She applies these same techniques in data science. As director of the Human-Centered Data Science Lab, Cecilia explains that every algorithm you write has potential human impact. A small error can be magnified and can have dramatic effects for thousands or millions of people.She has co-authored a book called Human Centered Data Science: An Introduction to help experienced and new data scientists learn how to plan for and manage the unintended consequences from the automated collection, analysis, and distribution of very large data sets. There are human decisions at every stage of the work of data science, and we discuss how bias and inequality may result from these choices and what to do to help prevent this. She says we need to put human needs and ethics at the center of data science and place data in its social context.RELATED LINKSConnect with Cecilia Aragon on LinkedIN and TwitterFind out more about Cecilia on her University of Washington profile pageFind out more about Cecilia's bookLearn more about the Human-Centered Data Science Lab at the University of WashingtonConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

Kristian’s interest in statistics and algorithmic fairness has taken her on a winding career path from academia to business, to public service, and back to academia. As she has made different career changes, she didn’t decide between academia vs. industry vs. non-profit, it was more about the problem she was interested in working on at the moment, and what else is happening in her life. After she earned her PhD in Statistical Science from Duke University, she worked as a research professor at Virginia Tech where she did microsimulation and agent-based modelingin a simulation lab. After that, she tried a data visualization and analytics startup called DataPad that was quickly acquired. When she was thinking about her next step in her career, she wanted to do something with social impact.She was fascinated by the work of the Human Rights Data Analysis Group (HRDAG) that was applying statistical models to casualty data to estimate the number of undocumented conflict casualties. She spent a summer working for HRDAG in Colombia and then decided to join the organization full time. She spent five years as HRDAG’s lead statistician leading the group’s project on criminal justice in the United States focused on algorithmic fairness and predictive policing. Predictive policing uses algorithms to help the police decide where to deploy their resources based on crime statistics, so if you look at where crimes are most likely to occur, this is where you police more often. Kristian’s work showed that these algorithms could actually perpetuate historical over-policing and racial bias in minority communities. Early this year, she moved from HRDAG back to academia. She started her new position at the University of Pennsylvania in the Computer and Information Science Department on March 2 and a week later Penn closed down for COVID. Over this year, she has learned that she needs to adjust her expectations for herself, and not be so frustrated when she can't get things done that maybe under normal circumstances she could. It's not just working from home with her daughter nearby, it's the stress of everything that's going on, the additional mental fatigue of having to do all these risks calculations. This year has also made her appreciate the increasingly critical role of data science in driving data-driven decision making.RELATED LINKSConnect with Kristian Lum on LinkedIN and TwitterLearn more about Penn EngineeringLearn more about HRDAGConnect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

A data scientist by training, Lillian brings a passion for human-centric machine learning and algorithmic effects to her role leading Spotify’s Personalized Home team. She also talks about the experience of leading her team from home during the pandemic.Lillian’s team of data scientists and user researchers collaborate with a mixed-methods research approach to try to discern user needs and create the best matches between listeners and creators. They observe and analyze user behaviors to develop product insights. Her team’s user researchers develop qualitative insights about user needs and then work with the data scientists to figure out the scale of this need or behavior. “What are users telling us they actually need in that moment by doing certain actions, by looking and exploring at certain recommended items?” She sees that Spotify can have a big impact on inspiring new experiences between listeners and the creators of music and podcasts. By sharing music and podcast stories with the world, Spotify often making a match between a listener and a creator for the first time. It could be the first time someone tried a podcast. “These aren't just pixels and files that you're moving around, you're really creating creative experiences for people, and you're sharing somebody's creative output with people. There's a cultural consequence to the things that we do.” Spotify’s recommendation systems work partly by design and part of it is random. She says it’s the most exciting area of research for her right now because it's stretching the limits of what a recommendation system can do. She explains that the kinds of recommendations you get from friends are contextualized and teaching a computer to do that is very difficult. It's even harder to measure if you're doing a good job, because you have to understand how to interpret the reactions that people are giving. Lillian also cares deeply about being inclusive and offering opportunities for all types of people to be included in the tech conversation. As a female mathematician and proud Puerto Rican, she has seen how the current tech culture can be difficult for some to adapt to. She believes that instead of trying to adapt, it’s more important for each person to be authentic and that the culture itself needs to adapt to welcome all voices and their technical talents. While it’s been challenging leading her new team completely over Zoom, she says she is being very intentional about what kind of leader she is and what kind of visible working mom she is. She thinks it’s harder for more junior colleagues because they don't have the opportunities for workplace mentorship. Overall, she thinks this shift to remote work will be empowering and an opportunity to give people more flexibility about where they can live and work.RELATED LINKSConnect with Lillian Carrasquillo on LinkedInConnect with Lillian Carrasquillo on Twitter (@lillian)Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedInFind out more about Margot on her Stanford Profile

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

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

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

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

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

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

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

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

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.

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

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

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

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

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

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

Ballan brings multiple perspectives to her current work on Connected Cars—drawing on expertise in data science and neuroscience gained during her ever-changing career in academia, entrepreneurship and consulting. Ballan grew up in Turkey where it’s not unusual for women to pursue careers as scientists. In her youth, she was inspired by Mission Impossible TV shows where agents used futuristic technologies like fingerprint recognition and iris detection. She also loved cars. “Those were the things that I was really interested in. And I think my journey started from there,” she explained during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. After studying engineering in college, she pursued her love of cars to work for Bridgestone in Turkey. “I love patterns. And our problem was the tire patterns, how we can identify the right pattern and then balance the car on that right pattern.” She left there for a chance to develop fingerprint recognition algorithms for a NATO-sponsored research center in Turkey. While she was drawn to technology, she was also fascinated with the brain and got a PhD in complex systems and neuroscience. “I always try to run away from technology because I like to work with people. One of the reasons that I picked neuroscience was I wanted to interact with people.” This diverse background has prepared her well for understanding both the human and technological perspectives on transforming the driving experience where the car is connected to everything. She says today the car is a platform, and we design the driving experience around sensors and data. She believes that within her lifetime we will see autonomous cars everywhere. And she says all data scientists should be trained in the ethics of AI as we need be very mindful how we are using these technologies to improve our lives. RELATED LINKS Connect with Meltem Ballan on Twitter (@meltball) and LinkedIn 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 GM and NATO Visit the WiDS Podcast online Listen and Subscribe to the WiDS Podcast on Apple Podcasts, Google Podcasts, Spotify, Stitcher

Data science and genetics are closely linked and have been for some time. But now, data science is playing an even larger role in genetics, a trend that is prompting researchers to look hard at their ethical responsibilities, says Chiara Sabatti, a professor of biomedical data science and statistics at Stanford University. As is the case in many other fields, geneticists have access to much more data than in the past, and because it is digitized, it can be mined. “Scientists rely on statisticians to mine this data and help them formulate hypotheses,” Sabatti said during an interview recorded for this year’s Women in Data Science podcast at Stanford. Truly understanding and interpreting this data correctly will become increasingly important for the public good as the relationship between accessibility and privacy continues to grow, she noted. Because there is such a wealth of data, there are potentially thousands of hypotheses that could be explored in some cases, an obviously unworkable situation. Data scientists need to determine which of the hypotheses drawn from the data are worth pursuing, says Sabatti. And that means developing new tools “to be able to confidently say to the scientist, ‘these are the hypotheses that you should follow up.’” Sabatti voiced her concerns about the public’s confidence in science. “I am really worried that as scientists we contribute to this by putting forward results that are not as solid as they should be,” she says. “The idea that data speaks by itself is an illusion. It's very important for us to find a way to communicate to the general public what are the challenges of the data analysis.” This is particularly true in genetics, especially in light of increasing fascination with commercial DNA testing, says Sabatti. “I think the public is not aware of all the consequences of putting their data, genetic or not, online and available for mining. I think it's up to us as scientists to try to communicate clearly what it is that we can do with this data and what are the opportunities that come from data sharing,” she says. Beyond genetics, Sabatti cited the need for “algorithmic fairness,” a new concept that seeks to eliminate biases and contribute to a more equitable understanding of data. She is also hopeful for the next generation of statisticians. “I actually look at this field in a very optimistic view. I am amazed by the intelligence and the knowledge of young people coming into it. I cannot keep up with my students or the students in other people's labs. There is a lot of energy, and there's going to be a lot of interesting knowledge that comes out of this investigation,” she says.

Career paths don’t always follow a straight line. Just ask Elena Grewal, whose education culminated in a PhD in education, but who became the head data scientist at Airbnb. In some ways, the leap wasn’t quite as daunting as it might sound. Grewal’s training at Stanford was interdisciplinary, including statistics and econometrics. “Often it’s more about words being different than about skills being different,” Grewal said in an interview recorded for Stanford’s Women in Data Science podcast. At one point, she began to study machine learning and initially thought it was very different from the work she was doing. “Then I started looking at what people do in machine learning, and I was like, ‘Oh, it’s logistic regression, it’s clustering analysis. I do that; we just call it something different,’” Grewal says. Whether it’s called data science or not, many different fields have some kind of quantitative component, and people in those fields who are using quantitative skills may well have the background to become a data scientist, she says. Employees who are not data scientists can learn to understand and use the data their companies collect. Grewal started “data university” at Airbnb, a program that teaches employees at all levels to work with data to do just that. “I don’t want people who have data to be the keepers of knowledge or power, but to share that and to enable every person to be able to think more critically and to be able to make conclusions themselves,” she says. Grewal’s team taught SQL – a standard language used to query databases – to employees and created a database they could use to access company data. Since Airbnb launched data university last year, hundreds of people from other companies have asked Grewal’s team to help them start similar programs. Although undoubtedly successful today, Grewal champions the importance of grit and believing in yourself as a student, as she herself struggled academically when she was younger. In middle school, a “teacher sat down with my parents and told us that I was a really nice kid and that I was going to be fine in life, but I was just never going to be a top student,” she says. She didn’t let it bother her. After working intensively on math with her father, a university professor, Grewal’s grades shot up and she graduated at the top of her class. “I think that was an important early experience: Where you are is not where you can be. It’s important to just work hard, do your best, and see where you can go and not feel limited,” Grewal says.

Consumers know Walmart as a retailing giant that has changed the face of retail in communities across America. But with a data store containing billions of queries and items, it’s also a laboratory for the company’s data scientists and IT professionals who mine and manage it. “We have data scientists embedded in every single team within the company,” says Sonu Durgia, group product manager for search and discovery at Walmart Labs. “Every function at Walmart, from the quality of groceries to the supply chain, has data science embedded in it,” she noted during an interview recorded for the Women in Data Science podcast at Stanford University. Because Walmart’s product catalog is immense, holding the attention of consumers and helping them find what they want to buy is a challenge. “We do not have your attention for the next several hours. We have to show you the right things very, very quickly. So it's a ranking and relevance problem right there, even though it's not coming from a query,” Durgia says. Explaining the insights of data scientists to the business and retail sides of Walmart, people who are not always conversant with technical issues is an important part of her job, she says. Her varied career path has provided her with the expertise to interact successfully with Walmart’s line of business executives. “My engineering degree gives me those tools to really understand the (algorithms) and work with these engineers and very savvy data scientists. My finance background gives me that bird's eye view, understanding what the key things are here,” she says. Because data science is still a male-dominated discipline, finding a role model can be difficult for women in the field. But technology, says Durgia, has enabled new ways for women to find role models. “Back in the day, you would just look at your peer group to find inspiration or even to solve some problems, ask about a concept you didn't get in class. But now YouTube is your teacher. Everything is available,” she says.

Data scientists are involved in a wide array of domains, everything from healthcare to cybersecurity to cosmology. Megan Price and her colleagues at the Human Rights Data Analysis Group (HRDAG), however, are using data science to help bring human rights abusers to justice. The nonpartisan group played a key role in the case of Edgar Fernando García, a 26-year-old engineering student and labor activist who disappeared during Guatemala’s brutal civil war. Price, the executive director of HRDAG, says the investigation took years, but their work led to the conviction of two officers who kidnapped Garcia and the former police chief who bore command responsibility for the crime. “It was one of the most satisfying projects that I’ve worked on,” she says. Price discussed the case in more detail as well as other cases she’s worked on over the years and the role data science played in an interview recorded for the Women in Data Science podcast recorded at Stanford University. For a recent project in Syria, Price’s group used statistical modeling and found information previously unobserved by local groups tracking the damage caused by the war. Similarly, in Mexico, she expects HRDAG to gain a better understanding of in-country violence by building a machine learning model to predict counties with a higher probability of undiscovered graves. Price hopes that in the future human rights and advocacy organizations will have their own in-house data scientists to further combat social injustices around the world, and she believes that data science will continue to play an important role in the field. She advises young people entering the field of data science and social change to learn a programming language, pick an editor and find mentors and cheerleaders to help them along the way.

Fiber optic cables that convey data at high speeds across the globe area is a well-known feature of modern technology. Now, university data scientists have found a unique use for them: monitoring earthquakes.Distributed across Stanford’s telecom infrastructure, the cables have become a seismic array that has already collected data on over 1,000 Bay Area earthquakes, says Eileen Martin, a recent alumnus of Stanford’s Institute for Computational and Mathematical Engineering, now Assistant Professor at Virginia Tech, whose research is focused on seismology. Martin and Nilah Monnier Ioannidis, a postdoctoral scholar concentrating on data science and genomics at Stanford, sat down to discuss the pivotal role of data in their research for the Women in Data Science podcast. Despite coming from different fields, both researchers tout the importance of data in academic research. Genomic sequencing requires vast amounts of data, but privacy concerns mandate important restrictions, Ioannidis says. Consequently, she is collaborating with outside institutions that have already amassed large stores of genomic data to understand its role in the field of genomics. Kaiser Permanente is among those collaborations; the company has already done a large-scale genomics study for Northern California. Martin says that being open with other researchers and sharing ideas is a real plus in the field. Ioannidis echoes these sentiments. While Martin acknowledges the risk that another researcher will use the shared information, she adds, “We’re all busy trying to do our own experiments.” Their advice for students looking to pursue a career in data science within academia: look for new experimental techniques because there will always be an interesting math or computing problem to solve.

“Fail fast” has become something of a mantra in Silicon Valley. But Janet George, the chief data officer of data storage giant Western Digital, has an amendment to that conventional wisdom: “Fail privately.”She suggests that failing privately allows you to open yourself up to discovery and exploration in a safe setting where you are able to take risks. “Carve out time for yourself so you can fail privately. So, you take 20 percent of your time in big initiatives you feel you can really contribute to, but take 20 percent of your time [(where you can])fail privately.” George, who has worked for some of the most important companies in the technology industry, shared this piece of advice, her career trajectory and the role of data science in the storage industry for the Women in Data Science podcast at Stanford University. Although the fear of failure is natural, it should never become a reason to avoid risk, she says. Taking an executive role at a storage company was a risk for George because she knew little about manufacturing before and. “I had to learn deeply about the device physics domain,.” she says. She became familiar with arcane matters like bit counts, failure rates, temperature testing and the impact of voltage on storage cells in order to ensure her success. Now in her fourth year at Western Digital, George continues to notice how much data science comes into play across the spectrum of the company’s business. From manufacturing to security and product development, “every aspect of mathematics, especially linear algebra, plays a very significant role,” she says. “When you think about the computations of scale, when you think about genetic algorithms, its applications, regression-type algorithms, or you even think about neural networks, it’s computationally heavy, it’s mathematically heavy.” Creating a die, essentially a mold, for a new storage device, for example, starts with tens of thousands of possible parameters. Data scientists at the company have to sift through a multitude of mathematical possibilities and discover the 20 or 25 most critical parameters. As the only woman at most executive meetings, George is wielding influence as a lone voice at the table, a skill honed over many years with important risks taken along the way. Her advice for aspiring data scientists: Build relationships and credibility within your organization and lead by example.

When Jennifer Widom began her career in computer science, it was a relatively narrow and specialized field. Three decades later, computer science has become an interdisciplinary field that touches on broad swaths of society and promises solutions to global problems such as healthcare and sustainability, she says. “Computer science used to be a niche. But (it) has become much more broadly used, broadly applicable across all fields. Instead of it just being a narrow study of software and hardware, it's now a lot about what you can use that software and hardware for in other fields,” says Widom. Indeed, learning about the relationships between math, computers and music prompted Widom to make a radical career change. Her undergraduate degree is in music, and she was on a path to become an orchestral trumpet player. But a course focused on computer applications for music was so intriguing she shifted her studies, eventually becoming a computer scientist and the dean of the School of Engineering at Stanford. Increasingly, jobs in industries related to computer science will be broader and encompass the need for data science at its core. “We’ll still need straight-line software engineers, but there will be more jobs for people with additional skills and interests,” Widom said in an interview recorded for the Women in Data Science podcast at Stanford University. That shift may well make the field more attractive to women, she says. Computer science has become so popular that nearly 20 percent of the student body at Stanford is majoring in it, and the university is struggling to keep up with demand, she says. Data science continues to play an important role in its continued evolution as more and more students use data to solve complex problems. But what do those students really want? “Are the students who are coming to computer science coming because they want to learn just the computer science, or are they coming because they want to apply computer science to their other interests? I'm going to venture a guess that the second is true for a lot of those students,”Widom says. If that’s the case, Stanford and other universities will need to shift the computer curriculum to be more reflective of its newly interdisciplinary nature, she says. Widom pioneered the use of MOOCs —massive open online courses —and says teaching them “was one of the most invigorating and exciting things I think I've done in my whole career.” The experience of reaching so many people —her first effort attracted 100,000 students —inspired her to take a sabbatical in which she traveled to under-developed countries offering free short-courses, workshops and roundtables, covering such topics as big data, collaborative problem-solving and women in technology. Her “instructional odyssey” was not only personally gratifying, but it shaped her teaching. “I think, based on my experience with the MOOCs and travel, that the way I could best influence people directly would be to show up and teach them,” she says. “I just really loved reaching people all over the world.”

Be yourself” was just one of the many career tips Caitlin Smallwood shared during a conversation with Stanford professor and Women in Data Science podcast host, Margot Gerritsen. Smallwood, vice president of data science and analytics at Netflix, urges up-and-coming data scientists to explore “the avenues and nooks and crannies” of the discipline and avoid limiting themselves to the most obvious paths. Smallwood is passionate about data-driven content and predicts that deep learning will continue to propel advances in applied data science in the future, specifically in the area of machine translation. It will take some time, she says, but machine translation would allow users to watch a movie or video and understand the subtleties of language and culture at a deeper level through nuances in inflection appropriate for different languages. Smallwood is interested in the ways that data science guides content and helps people “understand regions and cultures around the world through storytelling.” She enjoys the fact that her job allows her to engage and learn as well.“I, myself, have learned so many things from watching different pieces of content. You learn something that’s much more subliminal or that can really impact your empathy when you relate to a character and see the details of how they live their lives in an entirely different culture. And that’s different than reading a news article about a culture,” she says. As to her own future, Smallwood expects to stay at Netflix for a long time. “There are just such massive, new, exciting problems that we’re working on now, and I can’t imagine that changing.”

Attaining tenured status at a major university is often the culmination of an academic’s career; giving it up is unthinkable for most. But after 10 years at UCLA, Jennifer Chayes was offered a job at Microsoft. The offer, she says,“scared me to death,” but she took the job and is now managing director for Microsoft Research in New England, New York and Montreal. “There are brass rings that come along,and they always come along at the most inopportune times,and they look really scary, but I believe that we should grab them when they come along,” Chayes says during a conversation with Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Chayes is a big advocate of eliminating biases in search algorithms and believes that data scientists have “the opportunity to build algorithms with fairness, accountability, transparency and ethics, or FATE.” FATE, a group that formed at one of Chayes’ labs, works to address inequity in the field. In one particular instance, the group discovered that certain searches yielded certain results. Searches looking for computer programmers, for example, typically returned results for people with male names. The change Chayes' team implemented in the search algorithm removed that built-in bias. Removing bias from hiring is not only fair, it results in better outcomes, she says. “I think that you’re more likely to ask the right questions if you have been on the wrong side of outcomes. So you’re much more likely to see a lack of fairness or bias as a problem before it happens.” Chayes believes that the fieldof data science is changing and that the increase in underrepresented voices will be critical to the future of the field moving forward.