Flash Forward is a show about possible (and not so possible) future scenarios. What would the warranty on a sex robot look like? How would diplomacy work if we couldn’t lie? Could there ever be a fecal transplant black market? (Complicated, it wouldn’t, and yes, respectively, in case you’re curious.) Hosted and produced by award winning science journalist Rose Eveleth, each episode combines audio drama and journalism to go deep on potential tomorrows, and uncovers what those futures might re ...
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תוכן מסופק על ידי Women in Data Science Worldwide (WiDS), Professor Margot Gerritsen, and Chisoo Lyons. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Women in Data Science Worldwide (WiDS), Professor Margot Gerritsen, and Chisoo Lyons או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
דומה לWomen in Data Science Worldwide
Interviews with mathematics education researchers about recent studies. Hosted by Samuel Otten, University of Missouri. www.mathedpodcast.com Produced by Fibre Studios
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An award-winning cannabis podcast for women, by women. Hear joyful stories and useful advice about cannabis for health, well-being, and fun—especially for needs specific to women like stress, sleep, and sex. We cover everything from: What’s the best weed for sex? Can I use CBD for menstrual cramps? What are the effects of the Harlequin strain or Gelato strain? And, why do we prefer to call it “cannabis” instead of “marijuana”? We also hear from you: your first time buying legal weed, and how ...
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A science guy from the deep south (Destin) and a humanities guy from the wild west (Matt Whitman) discuss deep questions with varying levels of maturity.
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(Apple's Best of 2018) In-depth conversations with people at the top of their game. Jordan Harbinger unpacks guests' wisdom into practical nuggets you can use to impact your work, life, and relationships. Learn from leaders (Ray Dalio, Simon Sinek, Mark Cuban), entertainers (Moby, Tip "T.I." Harris, Dennis Quaid), scientists (Neil deGrasse Tyson, Bill Nye), athletes (Kobe Bryant, Dennis Rodman, Tony Hawk) and an eclectic array of fascinating minds, from art forgers and arms traffickers to sp ...
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DNA science. Artificial intelligence. Smartphones and 3D printers. Science and technology have transformed the world we live in. But how did we get here? It wasn’t by accident. Well, sometimes it was. It was also the result of hard work, teamwork, and competition. And incredibly surprising moments. Hosted by bestselling author Steven Johnson (“How We Got To Now”), American Innovations uses immersive scenes to tell the stories of the scientists, engineers, and ordinary people behind the great ...
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Everyone has a dream. But sometimes there’s a gap between where we are and where we want to be. True, there are some people who can bridge that gap easily, on their own, but all of us need a little help at some point. A little boost. An accountability partner. A Snooze Squad. In each episode, the Snooze Squad will strategize an action plan for people to face their fears. Guests will transform their own perception of their potential and walk away a few inches closer to who they want to become ...
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Embedded is the show for people who love gadgets. Making them, breaking them, and everything in between. Weekly interviews with engineers, educators, and enthusiasts. Find the show, blog, and more at embedded.fm.
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Learn something new every day! Everything Everywhere Daily is a daily podcast for Intellectually Curious People. Host Gary Arndt tells the stories of interesting people, places, and things from around the world and throughout history. Gary is an accomplished world traveler, travel photographer, and polymath. Topics covered include history, science, mathematics, anthropology, archeology, geography, and culture. Past history episodes have dealt with ancient Rome, Phoenicia, Persia, Greece, Chi ...
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Redefining AI is the 2024 New York Digital Award winning tech podcast! Discover a whole new take on Artificial Intelligence in joining host Lauren Hawker Zafer, a top voice in Artificial Intelligence on LinkedIn, for insightful chats that unravel the fascinating world of tech innovation, use case exploration and AI knowledge. Dive into candid discussions with accomplished industry experts and established academics. With each episode, you'll expand your grasp of cutting-edge technologies and ...
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<div class="span index">1</div> <span><a class="" data-remote="true" data-type="html" href="/series/good-moms-bad-choices-2913243">Good Moms Bad Choices</a></span>
If Oprah and Howard Stern had a baby it would be Good Moms Bad Choices. Meet Erica and Milah, two uncensored sex and cannabis-positive parents who are redefining what modern motherhood looks like. Warning, this is not your everyday mommy Podcast! This audio diary is not only for parents but women who feel alone in their good and bad choices. Prepare to laugh, cry, and be inspired. Fellas, you’re invited too! New episodes drop every Wednesday. Welcome to the Tribe! Instagram: @goodmoms_badchoices Watch our episodes on Youtube For bonus content ---> www.patreon.com/goodmomsbadchoices
Jane Lauder | Using Data Science to Create Aspirational Products
Manage episode 361214339 series 2706384
תוכן מסופק על ידי Women in Data Science Worldwide (WiDS), Professor Margot Gerritsen, and Chisoo Lyons. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Women in Data Science Worldwide (WiDS), Professor Margot Gerritsen, and Chisoo Lyons או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
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.
58 פרקים
שתפו
Manage episode 361214339 series 2706384
תוכן מסופק על ידי Women in Data Science Worldwide (WiDS), Professor Margot Gerritsen, and Chisoo Lyons. כל תוכן הפודקאסטים כולל פרקים, גרפיקה ותיאורי פודקאסטים מועלים ומסופקים ישירות על ידי Women in Data Science Worldwide (WiDS), Professor Margot Gerritsen, and Chisoo Lyons או שותף פלטפורמת הפודקאסט שלהם. אם אתה מאמין שמישהו משתמש ביצירה שלך המוגנת בזכויות יוצרים ללא רשותך, אתה יכול לעקוב אחר התהליך המתואר כאן https://he.player.fm/legal.
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.
58 פרקים
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1 Navigating Career Transitions: From Academia to Product Management with Sohini Sakhar 29:30
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיHighlights Time at Mathworks (17:27) Being a team leader (18:52) Staying in touch with the technical side (20:04) Advice (27:07) Bio Sohini Sarkar is a Principal Product Manager and a Senior Team Lead at MathWorks. Her career started with earning a Ph.D. in Civil Engineering and then working on DOE and EPA projects as a postdoctoral researcher. She has held various positions at Dassault Systèmes, ranging from a solution consultant to a market and competitive intelligence analyst, concurrently earning her MBA. Currently at MathWorks, Sohini focuses on established areas such as Math, Statistics, Optimization, and Machine Learning, to more emerging technologies such as Quantum Computing, Large Language Modeling and Generative AI, as well as Scientific Machine Learning. Connect with Sohini Sohini Sarkar on Linkedin Connect with Us Chisoo Lyons on LinkedIn Follow 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…
1 Predicting Responsibly: Claudia Perlich on AI, Bias, and the Art of Data Science 46:02
46:02 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי46:02
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיPredictive Modeling (4:15) Human judgement and processes (14:06) Imperfection in models (21:40) Bio Claudia Perlich is Managing Director and Head of Strategic Data Science for Investment Management at Two Sigma, where she has worked for seven years. In this role, Claudia is responsible for developing innovative alpha strategies at the intersection of alternative data, thematic hypotheses and machine learning in public markets. Claudia joined Two Sigma from Dstillery, an AI ad targeting company, where she worked as Chief Scientist. Claudia began her career in data science at the IBM Watson Research Center, concentrating on research in data analytics and machine learning for complex real-world domains and applications. Since 2011, Claudia has served as an adjunct professor teaching Data Mining in the M.B.A. program at New York University’s Stern School of Business. Claudia received a Ph.D. in Information Systems from Stern School of Business, New York University, holds an M.S. of Computer Science from Colorado University and a B.S. in Computer Science from Technical University Darmstadt, Germany. Connect with Claudia Claudia Perlich on Linkedin Connect with Us Margot Gerritsen on LinkedIn Follow 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…
1 Navigating Curiosity to Leadership in Tech 15:56
15:56 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי15:56
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיHighlights Vulnerability and honesty in leadership (12:07) Finding leaders who are willing to take risks on talent (13:48) Bio Maritza is an engineering manager at Klaviyo, where she leads a team focused on creating tools that help marketers better understand and optimize their campaign and flow performance. She enjoys collaborating with her team to develop solutions that provide clear, actionable insights for users. Before joining Klaviyo, Maritza worked at startups in the clinical trial management and natural language processing (NLP) sectors, where she gained experience in applying software to solve practical challenges. She began her career as a research assistant in a computational neuroimaging lab, where she was introduced to software development through the lab's open-source projects. This experience sparked her interest in using technology to address real-world problems. Outside of work, Maritza enjoys knitting, though much of her time is happily spent caring for her 1- and 3-year-olds. Connecting with Maritza Maritza Ebling on Linkedin Connect with Us Tina Tang on LinkedIn Follow 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…
1 From Graphs to Growth: Mentorship, Math, and the Power of Algorithms 36:31
36:31 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי36:31
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיMath and Computer Science (2:51) Graph Alignment (20:38) Bio Huda Nassar is a senior computer scientist at RelationalAI working on building the graph algorithms library offered as part of RelationalAI's product. Previously, Huda obtained a PhD in Computer Science from Purdue University and was a postdoc fellow at Stanford's School of Medicine. Huda is also known for her "Julia for Data Science" course which had over 13,000 students and focused on Data Science methods including graph analytics. Connect with Huda Huda Nassar on Linkedin Connect with Us Margot Gerritsen on LinkedIn Follow 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…
1 Beyond Borders: Elevating Women in Data Science and Leadership 36:16
36:16 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי36:16
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיLanguage as the access to different cultures (2:09) Career journey (8:23) People first in leadership style (23:19) Bio Hannah Pham is a seasoned data leader with experience building and scaling data teams at top tech companies like Airbnb and Pinterest. Hannah's expertise spans consumer and monetization domains. As the Head of Data Science for the consumer area at Pinterest, she leverages data to bring the best experience to Pinners and drive business growth. Hannah is also a successful startup founder with Skin AI, a personalized skincare company that she co-founded in 2018. Connect with Hannah Hannah Pham on Linkedin Connect with Us Chisoo Lyons on LinkedIn Follow 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…
1 From Internship to Impact: A Data Scientist’s Journey 28:21
28:21 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי28:21
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSydney 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. Highlights Internship at Ford (8:25) Time at Notre Dame (14:55 Influencers (17:54) Bio Sydney 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 Sydney Sydney Hazen on Linkedin Connect with Us Chisoo Lyons on LinkedIn Follow 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…
1 The Power of Linguistics in Large Language Models and AI 42:46
42:46 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי42:46
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיHighlights LLMs (1:46) AI systems (3:25) The need for humanness in AI (20:17) Transitioning to independent consultant (28:31) Bio Karin 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 Karin Karin Golde on Linkedin Website West Valley IA Connect with Us Chisoo Lyons on LinkedIn Follow 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…
1 Applying topological data analysis and geometry-based ML 28:24
28:24 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי28:24
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיHighlights: 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 Us Margot Gerritsen on LinkedIn Listen and Subscribe to the WiDS Podcast on Apple Podcasts , Google Podcasts , Spotify , Stitcher…
1 Using Curiosity, Mentorship, and Education to Build a Career 34:36
34:36 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי34:36
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSummary: 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 LinkedIn Follow 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…
1 Fighting Crypto Crime with Data Science 33:35
33:35 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי33:35
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיIn 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 crime Trust in stable coin Misconceptions around cryptocurrency Using 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: Chainalysis New York City Economic Development Corporation Connect with Us Margot Gerritsen on LinkedIn Follow 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…
1 Using Storytelling to Communicate with Stakeholders 40:27
40:27 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי40:27
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיMichelle 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 LinkedIn Bios: 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 Us Chisoo Lyons on LinkedIn Follow 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…
1 Data Science Leadership: Creating Meaningful Impact 43:15
43:15 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי43:15
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיIn 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 LinkedIn Bios: 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 Us Chisoo Lyons on LinkedIn Follow 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…
1 Kate Kolich on Mentorship, Data Ethics, and Leadership 31:47
31:47 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי31:47
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיKate 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 LINKS Connect with Kate Kolich on LinkedIn Find out more about the Reserve Bank of New Zealand View 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 LinkedIn Follow 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…
1 Breaking Barriers to Entry & Success for Women in Tech with Telle Whitney 31:35
31:35 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי31:35
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיTelle 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 LINKS Connect with Telle Whitney on LinkedIn Find out more about AnitaB.org Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Follow 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…
1 Srujana Kaddevarmuth | Opening New Realms of Data Science and AI 35:52
35:52 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי35:52
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSrujana 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 LINKS Connect with Srujana Kaddevarmuth on LinkedIn Find out more about Walmart Learn more about the United Nations Association San Francisco Chapter Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Follow 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…
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Women in Data Science Worldwide
1 Veronica Edwards | The Bridge Between Dance and Data Science 28:19
28:19 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי28:19
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיToday 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 LINKS Connect with Veronica Edwards on LinkedIn Find out more about Polygence Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Follow 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…
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Women in Data Science Worldwide
1 Jane Lauder | Using Data Science to Create Aspirational Products 27:21
27:21 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי27:21
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAs 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.…
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Women in Data Science Worldwide
1 Priya Donti | Using AI to Fight the Climate Crisis 41:49
41:49 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי41:49
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAn 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 LINKS Connect with Priya on LinkedIN Find out more about the Climate Change AI Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Lesly Zerna | Teaching and learning data science in Latin America (Spanish) 47:27
47:27 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי47:27
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיLesly 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 LINKS Connect with Lesly on LinkedIN Find out more about the Universidad Privada Boliviana Connect with Cindy Orozco Bohorquez on LinkedIN…
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Women in Data Science Worldwide
1 Leda Braga | Applying data science to investment strategies 36:37
36:37 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי36:37
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיLeda 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 LINKS Connect with Leda on LinkedIn or Twitter Find out more about Systematica Investments Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile …
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Women in Data Science Worldwide
1 Jessica Bohórquez | Using AI for leak detection in water pipelines (Spanish) 46:09
46:09 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי46:09
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיA 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 LINKS Connect with Jessica on LinkedIN Find out more about the University of Adelaide Connect with Cindy Orozco Bohorquez on LinkedIN…
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Women in Data Science Worldwide
1 Karolina Urbanska | Using data science to study human behavior 47:55
47:55 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי47:55
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAs 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 LINKS Connect with Karolina on LinkedIn or Twitter Find out more about Teach First Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Welcoming our new podcast co-host, Cindy Orozco 51:08
51:08 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי51:08
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיEPISODE NOTES WiDS 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 LINKS Connect with Cindy Orozco on LinkedIN Find out more about Cerebras Systems Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Tahu Kukutai | Advocating for indigenous data sovereignty 38:17
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיWhen 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 LINKS Connect with Tahu on LinkedIN and Twitter Find 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 LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Allison Koenecke | Researching algorithmic fairness and causal inference in public health 27:31
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAllison 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 LINKS Connect with Allison on LinkedIN and Twitter Find out more about the Microsoft Research Machine Learning and Statistics group Find out more about Cornell University Information Science Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Karina Edmonds | Building bridges between business and academia 38:02
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיThough 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 LINKS Connect with Karina on LinkedIn and Twitter Find out more about SAP Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Fatima Abu Salem | Applying data science for the public good in Lebanon 33:39
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיFatima 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 LINKS Connect with Fatima on LinkedIn and Twitter Find out more about American University of Beirut (AUB) Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Louvere Walker-Hannon | Gaining skills and overcoming barriers to a career in data science 48:00
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיLouvere 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 LINKS Connect with Louvere on LinkedIn and Twitter Find out more about Mathworks Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Menglin Cao | Data science in fintech and financial services 29:52
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAfter 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 LINKS Connect with Menglin Cao on LinkedIN Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Karen Hao | Covering AI and Ethics Washing in the Tech Industry 34:20
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיKaren 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 LINKS Connect with Karen Hao on LinkedIN and Twitter Find out more about Karen on her website Find out more about MIT Technology Review Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Cecilia Aragon | Aerobatic Pilot, Author and Data Scientist 32:13
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיThe 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 LINKS Connect with Cecilia Aragon on LinkedIN and Twitter Find out more about Cecilia on her University of Washington profile page Find out more about Cecilia's book Learn more about the Human-Centered Data Science Lab at the University of Washington Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Kristian Lum | Applying Statistics to Promote Fairness and Transparency 30:50
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיKristian’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 LINKS Connect with Kristian Lum on LinkedIN and Twitter Learn more about Penn Engineering Learn more about HRDAG Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Lillian Carrasquillo | Using Human-Centric Data Science at Spotify 38:22
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הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיA 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 LINKS Connect with Lillian Carrasquillo on LinkedIn Connect with Lillian Carrasquillo on Twitter (@lillian) Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Femke Vossepoel | Applying Data Assimilation Tools to COVID Forecasting Models 39:28
39:28 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי39:28
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיAfter 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 LINKS Connect with Femke Vossepoel on LinkedIn Find out more about Femke on her TU Delft profile Read more about TU Delft Civil Engineering and Geosciences Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Francesca Dominici + Rachel Nethery | Using Data Science to Study Air Pollution Effect on COVID-19 Outcomes 34:39
34:39 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי34:39
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיWith 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 LINKS Connect with Francesca Dominici on Twitter (@francescadomin8) and LinkedIn Find out more about Francesca on her Harvard Profile Connect with Rachel Nethery at Harvard Read more about Harvard T.H. Chan School of Public Health Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile…
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Women in Data Science Worldwide
1 Manisha Desai | The Importance of Data Integrity in COVID-19 Clinical Trials 38:04
38:04 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי38:04
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיManisha 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 LINKS Connect with Manisha Desai on LinkedIn Find out more about Manisha on her Stanford Profile Read more about Stanford School of Medicine 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…
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Women in Data Science Worldwide
1 Newsha Ajami | Improving Urban Water Systems Through Data Science, Public Policy and Engineering 33:03
33:03 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי33:03
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיNewsha 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 LINKS Connect with Newsha Ajami on LinkedIn Read more about Stanford University's Water in the West Read more about Stanford Woods 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…
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Women in Data Science Worldwide
1 Andrea Gagliano | The Intersection of Arts and Technology 31:37
31:37 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי31:37
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיWhen 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 LINKS Connect with Andrea Gagliano on LinkedIn Read more about Getty Images 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…
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Women in Data Science Worldwide
1 Ya Xu | Using Data To Create Economic Opportunities For All Members Of Global Workforce 40:09
40:09 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי40:09
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיYa 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 LINKS Connect with Ya Xu on LinkedIn Read more about LinkedIn Engineering 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…
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Women in Data Science Worldwide
1 Susan Athey | Bringing an Economist’s Perspective to Data Science 51:30
51:30 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי51:30
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיWith 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 LINKS Connect with Susan Athey on Twitter (@Susan_Athey) and LinkedIn Find out more about Susan on her Stanford GSB Profile Read more about Stanford Graduate School of Business 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…
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Women in Data Science Worldwide
1 Montse Medina | Lessons Learned Building a Data Science Startup 36:56
36:56 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי36:56
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיMontse 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 LINKS Connect with Montse Medina on Twitter (@montsechka) and LinkedIn Read more about Deloitte Espana and Jetlore 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…
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Women in Data Science Worldwide
1 Bonus Episode: Margot Gerritsen | How to Get More Women Into Data Science 28:04
28:04 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי28:04
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיIt 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 LINKS Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile Connect with Russ Altman on Twitter (@Rbaltman) and LinkedIn Find out more about Russ on his Stanford Profile Read more about Stanford ICME and Stanford School of Engineering This podcast episode was originally published on Stanford Engineering's Future of Everything podcast .…
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Women in Data Science Worldwide
1 Timnit Gebru | Advocating for Diversity, Inclusion and Ethics in AI 36:23
36:23 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי36:23
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיTimnit 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 LINKS Connect with Timnit Gebru on Twitter (@TimnitGebru) and LinkedIn Read more about Google AI and Black in AI 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…
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Women in Data Science Worldwide
1 Christiane Kamdem + Lama Moussawi | WiDS Ambassadors Bring Education and Role Models to their Communities 30:43
30:43 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי30:43
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיOur WiDS Ambassadors in Paris and Beirut discuss the impact of the growing WiDS presence and communities in their countries. Christiane Kamdem, a native of Cameroon and WiDS Ambassador in Paris, is a senior data scientist at the French energy company Total where she analyzes data to create new services and improve market impact. WiDS Beirut Ambassador Lama Moussawi is an Associate Professor at the Olayan School of Business at the American University of Beirut (AUB) where she conducts research and teaches management science. Both women became WiDS ambassadors because they believe that role models, education, and community can make a real impact. “I believe in the vision of WiDS, which is to inspire, educate, and get educated in the field of data science, and to encourage and support more women and girls to join the field,” Lama says during a conversation with Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Lama grew up in in Lebanon at a time of war. “It wasn't expected that girls would go to university. Girls were expected to get married and be mothers. So when I applied to universities and I got accepted at AUB with a scholarship, it was a great opportunity that I went for and then things started from there,” she says. She explains that young girls in the Middle East are very smart and have the quantitative skills, but they need support. WiDS provides support, guidance, and mentorship. “By showcasing role models, stellar female experts in the field, we are encouraging those young girls to not to be afraid to join,” she says. “Events like WiDS help us defy those barriers and those challenges that exist for women.” Christiane says the majority of the students in her grad school in Cameroon were men, and even now, she is the only woman on a team of five data scientists. After getting her Master’s degree, she started to participate in events to attract more young women in STEM fields. “It's very important to inspire the next generation, and it's important to build a kind of network of data scientists that can be models for the next generation. Because when you have a model, you want to be like this model,” says Christiane. Both women have helped to host regional WiDS events that are making an impact in their local communities. The most recent WiDS event in Paris had nearly 250 participants. Christiane says she not only gained a lot of technical knowledge about data science, she also heard the stories from many women who had to struggle in order to be where they are now. “It was very instructive to hear that various paths can lead to great achievements,” she says. Over the past two years the WiDS events at AUB in Beirut have gotten bigger and generated more awareness. In 2019, many more people were interested and wanted to attend the event. “We are seeing a lot of institutional support, and huge support of the local community,” says Lama. “More and more companies are participating, sending their employees, and contacting us to work on initiatives related to supporting women to join the data science field.” RELATED LINKS Connect with Christiane Kamdem on LinkedIn Connect with Lama Moussawi on Twitter (@lama_moussawi) and LinkedIn Read more about WiDS Paris and WiDS Beirut Read more about Total and the American University at Beirut Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile Find out more about Margot on her personal website…
W
Women in Data Science Worldwide
1 Sherrie Wang | Applying Machine Learning to Solve Global Food Security Challenges 31:22
31:22 הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתי31:22
הפעל מאוחר יותר הפעל מאוחר יותר רשימות לייק אהבתיSherrie brings an interdisciplinary and entrepreneurial perspective to her research that she has developed through her work in the fields of computational finance, biomedical engineering, and computer vision. Sherrie explains that about 1 in 9 people do not have access to adequate food. She is using satellite imagery and machine learning to identify and map crops around the world, see where people are most vulnerable, and what interventions or policies have the greatest effect. “There are a lot of problems that technology alone can't solve and we still need to understand the roots of a lot of the problems. That involves talking to people in the earth sciences and agriculture and learning from them. In those conversations, data science just becomes a tool, a very useful tool but it's a tool in the context of some much larger problem,” she explained to Stanford’s Margot Gerritsen, Stanford professor and host of the Women in Data Science podcast. Sherrie has brought her expertise to the WiDS Datathon committee where she helped develop the 2019 challenge. Participants were asked to use satellite data to identify oil palm plantations. She felt it was important to bring awareness to this issue as these plantations are contributing to the destruction of the rain forests. Sherrie’s curiosity has taken her down many different paths so far. If there’s a field she wants to learn more about, she has never hesitated jumping in. She admits these transitions can be challenging and each one comes with a steep learning curve. While she felt overwhelmed early on in grad school, she has learned to succeed through patience and consistent effort. Her entrepreneurial experience running a tutoring service as an undergrad has also prepared her well for her grad school research. She says that grad school and entrepreneurship are similar in many ways. “You're creating something new that hasn't existed before, so there are a lot of different directions you can always be going. Realizing that there are all these options out there and it's okay to be brave and pick one and go with it and see how it goes and it's okay to fail, that was a big lesson from that experience,” she says. She is excited about figuring out the new science she can explore that will have an important impact on how we see the world. She has found her research and teaching so satisfying that she wants to pursue a career in academia. She believes that machine learning and environmental science are critical to our future. “I am optimistic that if we're honest about the sorts of problems we face, then we can collectively, creatively solve these problems. It goes back again to being patient and being consistent,” she says. “The only choice we really have is to keep trying to solve these problems one day at a time.“ RELATED LINKS Connect with Sherrie Wang on Twitter (@sherwang) and LinkedIn Find out more about Sherrie on her Stanford Profile Read more about Stanford ICME Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn Find out more about Margot on her Stanford Profile Find out more about Margot on her personal website…
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