Next week the RE•WORK team are heading to San Francisco to host the biggest Deep Learning Summit to date. As a strong advocate for supporting female entrepreneurs and women working towards advancing technology and science, we are also holding a Women in AI Dinner to champion progressions in AI, with a focus on leading women in the field. The dinner is open to both genders and will provide the opportunity for AI experts from all industries to network and learn from each other. In advance of these events, we’ve spoken to many of the most influential women working in AI in the USA, and we'd like to take this chance to highlight their work.
- Fei-Fei Li, @drfeifei
Fei-Fei is an Associate Professor at the CS Dept. at Stanford and the Director of the Stanford Artificial Intelligence Lab and the Stanford Vision Lab, where she works with the most brilliant students and colleagues worldwide to build smart algorithms that enable computers and robots to see and think, as well as to conduct cognitive and neuroimaging experiments to discover how brains work. She received Ph.D. from Caltech.
- Timnit Gebru, @timnitgebru
Timnit is a research scientist in the Ethical AI team at Google and just finished her postdoc in the Fairness Accountability Transparency and Ethics (FATE) group at Microsoft Research, New York. Prior to that, she was a PhD student in the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. Her main research interest is in data mining large-scale, publicly available images to gain sociological insight, and working on computer vision problems that arise as a result, including fine-grained image recognition, scalable annotation of images, and domain adaptation. She is currently studying the ethical considerations underlying any data mining project, and methods of auditing and mitigating bias in sociotechnical systems. The New York Times, MIT Tech Review and others have recently covered her work. As a cofounder of the group Black in AI, she works to both increase diversity in the field and reduce the negative impacts of racial bias in training data used for human-centric machine learning models. Timnit will be sharing her latest work on the Ethics & Social Responsibility Stage at the Deep Learning Summit in San Francisco next week.
- Chelsea Finn, @chelseabfinn
Chelsea is a research scientist at Google Brain and post-doctoral scholar at Berkeley AI Research. Starting in 2019, she will join the faculty in CS at Stanford University. She is interested in how learning algorithms can enable machines to acquire general notions of intelligence, allowing them to autonomously learn a variety of complex sensorimotor skills in real-world settings. She received her PhD in CS at UC Berkeley in 2018 and her Bachelors in EECS at MIT in 2014. Chelsea will be sharing her latest work on the Deep Learning Stage at the Deep Learning Summit in San Francisco next week.
- Alison Darcy, @alisonmdarcy
Alison is the CEO and Founder of Woebot Labs Inc. a company that aims to make the best psychological tools radically accessible for everyone. She created Woebot, a friendly AI-powered chatbot that delivers cognitive behavior therapy at scale. A clinical research psychologist, Alison was Faculty in Psychiatry and Behavioral Sciences at the Stanford School of Medicine for a decade where she developed digital health interventions for young people. With both a technical and scientific background, Alison works at the intersection of mental health and technology. Alison also joined RE•WORK on the Women in AI Podcast last January.
- Joy Buolamwini , @jovialjoy
Joy is a poet of code on a mission to show compassion through computation. She is a Rhodes Scholar, Fulbright Fellow, Google Anita Borg Scholar, Astronaut Scholar, A Stamps President's Scholar and Carter Center technical consultant recognized as a distinguished volunteer. She holds a master's degree in Learning and Technology from Oxford University and a bachelor's degree in Computer Science from the Georgia Institute of Technology.
- Danah Boyd, @danahboyd
Dhana is a Principal Researcher at Microsoft Research and the founder of Data & Society. She’s a Visiting Professor at New York University's Interactive Telecommunications Program, and an academic and a scholar with research examining the intersection between technology and society. For over a decade, her research focused on how young people use social media as part of their everyday practices. More recently, Danah has turned to focus on understanding how contemporary social inequities relate to technology and society more generally. She’s collaborating with researchers working on topics like media manipulation, the future of work, fairness and accountability in machine learning, combating bias in data, and the cultural dynamics surrounding artificial intelligence.
- Kate Crawford, @katecrawford
Kate is a leading researcher, academic and author who has spent the last decade studying the social implications of data systems, machine learning and artificial intelligence. She is a Distinguished Research Professor at New York University, a Principal Researcher at Microsoft Research New York, and a Visiting Professor at the MIT Media Lab. Her recent publications address data bias and fairness, social impacts of artificial intelligence, predictive analytics and due process, and algorithmic accountability and transparency.
- Aleatha Parker-Wood, @aleatha
Aleatha leads a team of Ph.D. researchers dedicated to protecting you and your data, using state-of-the-art ML and security expertise. In addition to research, Aletha also teaches and mentor,s helping bring up the next generation of scientists and engineers. Feel free to connect if you're interested in networking or mentoring.
- Yinyin Liu, @yinyin_liu
Yinyin is Head of Data Science at Intel AI. She works to provide data science knowledge and expertise to AI products design whilst converging data science with research, and connecting with the community through open-source libraries and frameworks. Yinyin works with the team to build capabilities in various areas in NLP, RL, etc and apply data science through the stack on foundational AI software and hardware. She also works with partners and customers to apply AI to solve domain problems. Skilled in machine learning algorithms and research, data science, software engineering and customer collaborations, she also has a Ph.D focusedonn Machine Learning.
- Julia Hu,@JuliaHuCEO
Having been too often sleep deprived and dropping off exercise and diet regimens, Julia's goal is to make people happier and healthier as the CEO of lark technologies. Named Top 10 Most Innovative CE Companies in the World by Fast Company, lark is a mobile and wireless technology company that uses behavior change to personally coach people to feel great. A serial entrepreneur, she has focused on growing early-stage startups in the consumer products and cleantech space. Prior to lark, she ran global startup incubator Clean Tech Open, her own green buildings startup, and worked in China to build consumer electronics with D.light Design.
- Claire Delaumay, @lullabeee
Claire is vice president of engineering at NVIDIA, where she is responsible for the Isaac robotics initiative and leads a team to bring Isaac to market for roboticists and developers around the world. Prior to joining NVIDIA, Delaunay was the director of engineering at Uber, after it acquired Otto, the startup she co-founded. She was also the robotics program lead at Google and founded two companies, Botiful and Robotics Valley. Claire has 15 years of experience in robotics and autonomous vehicles leading teams ranging from startups and research labs to Fortune 500 companies. Claire holds a Master of Science in computer engineering from École Privée des Sciences Informatiques (EPSI).
- Jana Eggers, @jeggers
CEO of Nara Logics, a neuroscience-inspired AI company, providing a platform for recommendations and decision support. Her career has taken her from 3-person business beginnings to 50,000-person enterprises. She opened the European logistics software offices as part of American Airlines, dove into the internet in 1996 at Lycos, founded Intuit’s corporate Innovation Lab, helped define mass customization at Spreadshirt, and researched conducting polymers at Los Alamos National Laboratory.
- Carla P. Gomes
A professor of Computer Science, Carla’s research has covered several areas in AI, including complete randomized search methods, optimization, and machine learning. Carla is also the founding director of the Institute for Computational Sustainability and is noted for her pioneering work in developing computational methods to address challenges in sustainability. In 2007 Carla was elected a Fellow of the Association for the Advancement of Artificial Intelligence "for significant contributions to constraint reasoning and the integration of techniques from artificial intelligence, constraint programming, and operations research". She obtained a PhD. in computer science in the area of artificial intelligence and operations research from the University of Edinburgh. She also holds an M.Sc. in applied mathematics from the University of Lisbon.
- Rumman Chowdhury, @ruchowdh
Rumman comes from a quantitative social science background, and is a practicing data scientist. As a Senior Principal at Accenture, she works on cutting-edge applications of AI and is the Global Lead for Responsible AI. Rumman also leads client solutions on ethical AI design and implementation, working with organisations such as the IEEE and World Economic Forum. She has been named a fellow of the Royal Society for the Arts and is one of BBC’s 100 most influential women of 2017. In January Rumman shared her knowledge at the RE•WORK Deep Learning Summit and moderated the fireside chat with Daphne Koller, discussing AI in healthcare and the lack of minorities working in technology.
- Rosalind W. Picard, @RosalindPicard
In 1991 Rosalind joined MIT as a professor, and during her career there has founded and is director of the Affective Computing Research Group, as well as co-director of the Media Lab's Advancing Wellbeing Initiative. Picard also co-founded Affectiva, where she worked as Chief Scientist until 2013. In 2014 Picard then co-founded Empatica, where she is currently Chief Scientist and Chairman. Empatica has combined machine learning with wearable sensors to create 2 wearable healthcare products. Throughout her career Rosalind has been awarded with various best paper prizes and authored the book Affective Computing.
- Sara Hooker, @sarahookr
Sara is an AI Resident at Google Brain doing deep learning research. Her research interests gravitate towards algorithm interpretability, security and model compression research for mobile first AI. She believes in the power of data for good. Sara presented at the RE•WORK Deep Learning Summit in Toronto in 2018.
- Cathy Pearl, @cpearl42
Before joining Google, Cathy was the VP of user experience at Sensely, whose virtual nurse avatar, Molly, helps people engage with their health. Cathy is the author of the O’Reilly book “Designing Voice User Interfaces”. Throughout her career, Cathy has applied her knowledge to a range of projects, and has worked on everything from helicopter pilot simulators at NASA to a conversational iPad app in which Esquire magazine’s style columnist tells you what you should wear on a first date. During her time at Nuance and Microsoft, Cathy designed VUIs for banks, airlines, and Ford SYNC. Cathy holds a BS in cognitive science from UCSD and an MS in computer science from Indiana University. Cathy will be sharing her expertise at the Deep Learning Summit in San Francisco this January on the AI Assistant Stage.
- Been Kim, @_beenkim
Research Scientist, Google Brain
Before joining Google Brain, Been was a research scientist at Institute for Artificial Intelligence (AI2) and an affiliate faculty in the Department of Computer Science & Engineering at the University of Washington. Her research focuses on building interpretable machine learning, with the vision to make humans empowered by machine learning. She received her PhD. from MIT. Prior to her PhD, she worked at the MathWorks as a software engineer. Been spoke at the Women in Machine Intelligence Dinner in San Francisco last January.
- Hanna Wallach, @hannawallach
Senior Researcher, Microsoft Research
Hanna’s research is in the interdisciplinary field of computational social science, and has developed machine learning and natural language processing methods for analyzing the structure, content, and dynamics of social processes. Hanna’s work has been recognised throughout the industry, having won the best paper award at the Artificial Intelligence and Statistics conference in 2010, and named as one of Glamour magazine’s “35 Women Under 35 Who Are Changing the Tech Industry” in 2014. Hanna has also worked to increase diversity and has worked for over a decade to address the underrepresentation of women in computing, having co-founded two projects to increase women’s involvement in free and open source software development - the Debian Women Project and the GNOME Outreach Program for Women.
- Beena Ammanath, @beena_ammanath
Global VP - Artificial Intelligence, Data and Innovation, Hewlett Packard Enterprise
Prior to her current role at Hewlett Packard Enterprise, Beena was the Vice President of Data Science and Innovation at GE, working to transform the company into a digital industrial company leveraging data, analytics and AI. Beena has shared her expertise in technology as a board advisor in various Artificial Intelligence companies, including Predii, which uses patented Machine Learning technology to provide ‘Repair Intelligence”. Beena has also been leading efforts to increase diversity in technology, having been the Board Director of ChickTech, a non-profit dedicated to increasing the number of women and girls pursuing technology-based careers, and retaining women already in STEM workforces. In 2017 Beena founded nonprofit organization Humans For AI, which focuses on increasing diversity in technology. In 2016 Beena chatted to RE•WORK about breakthrough technology and achieving gender equality in the industry. You can read the interview here.
- Carol Reiley, @robot_MD
Formed out of Stanford’s AI Lab, drive.ai builds Deep Learning software for self-driving cars. As co-founder and president, Carol led the product team, built investor relationships to raise over $70 million and led the company strategy. Carol has eight technical patents, and is the author of more than a dozen papers published in various scientific conference proceedings, refereed journals and conferences, with a research focus on ‘intelligent robotic systems that can aid humans in performing skillful tasks more effectively.’ Carol has previously served on the various boards, including the IEEE Robotics and Automation Society and JHU Engineering Diversity Council. Carol has built up a large collection of awards over her career, and has been listed in the Silicon Valley Business Journal Most Influential list in 2017 and Inc Magazine’s Most Innovative Women Entrepreneurs.
- Cynthia Breazeal, @cynthiabreazeal
Cynthia is an Associate Professor of Media Arts and Sciences at the Massachusetts Institute of Technology where she founded and directs the Personal Robots Group at the Media Lab. She is also founder and Chief Scientist of Jibo, Inc. She is a pioneer of Social Robotics and Human Robot Interaction. She authored the book Designing Sociable Robots, and she has published over 100 peer-reviewed articles in journals and conferences on the topics of Autonomous Robotics, Artificial Intelligence, Human Robot Interaction, and Robot Learning. She serves on several editorial boards in the areas of autonomous robots, affective computing, entertainment technology and multi-agent systems. She is also an Overseer at the Museum of Science, Boston.
- Rana el Kaliouby, @kaliouby
Computer scientist, technologist, entrepreneur and business leader, Dr. el Kaliouby believes that “humanizing technology gives us a golden opportunity to re-imagine how we connect with machines, and, therefore, how we connect with each other.” Co-founder and CEO of Affectiva, the pioneer of Emotion AI, she invented the company’s award-winning emotion recognition technology. The Emotion AI platform combines facial expression and tone of voice to infer how a person is feeling, using deep learning and the world’s largest emotion data repository of more than five million faces, analyzed from 75 countries. Teaching machines to measure and interpret human emotions has the potential to dramatically improve lives, with such powerful applications as assisting doctors and nurses in delivering better care, engaging students and personalizing their learning experience, and increasing road safety by making “emotionally aware” vehicles. It also promises to forever change the rules of consumer engagement by providing real-time insight into viewers’ emotional responses to brands, ads and other digital content.
- Daniela Rus
Daniela Rus is the Andrew (1956) and Erna Viterbi Professor of Electrical Engineering and Computer Science and Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. Rus’s research interests are in robotics, mobile computing, and data science. Rus is a Class of 2002 MacArthur Fellow, a fellow of ACM, AAAI and IEEE, and a member of the National Academy of Engineering, and the American Academy for Arts and Science. She earned her PhD in Computer Science from Cornell University.
- Daphne Koller, @DaphneKoller
Daphne is the Chief Computing Officer at Calico Labs, an Alphabet (Google) company that is using advanced technology to understand aging and design interventions that help people lead longer, healthier lives. She is also the Co-Chair of the Board and Co-Founder of Coursera, the largest platform for massive open online courses (MOOCs). Previously, she was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years. In January Daphne become the founder and CEO of Insitro, incorporating machine learning into drug discovery. She is the author of over 200-refereed publications appearing in venues such as Science, Cell, and Nature Genetics. At the beginning of the year, Daphne took part in a fireside chat at the RE•WORK Deep Learning Summit in San Francisco, where she spoke about the implementation of AI in healthcare, saying “...if we can provide basic medical care with AI, we can leave the higher end of medical issues to the experts and people can receive high level medical care from experts at a lower cost.” Daphne appeared in a fireside chat at the Deep Learning Summit in San Francisco last January, 2018.
- Devi Parikh, @deviparikh
Devi Parikh is an Assistant Professor in the School of Interactive Computing at Georgia Tech, as well a Research Scientist at Facebook AI Research (FAIR). With interests in computer vision and visual recognition problems, her recent work involves exploring problems at the intersection of vision and language, and leveraging human-machine collaboration for building smarter machines. She received her Ph.D. from Carnegie Mellon University in 2009. Devi’s contributions in the field have been widely recognised by the community, and she is a recipient of numerous awards, including the NSF CAREER award, and an IJCAI Computers and Thought award. Devi has also featured in a Forbes' list of 20 "Incredible Women Advancing A.I. Research." She was also awarded a Marr Best Paper Prize at the International Conference on Computer Vision. Devi spoke at the RE•WORK Women in AI Dinner in San Francisco in 2018.
- Meredith Whittaker, @mer__edith
The AI Now Institute at NYU is an interdisciplinary research center dedicated to understanding the social implications of artificial intelligence. AI Now's work focuses on four core domains which are rights and liberties; bias and inclusion; labor and automation; and critical infrastructure and safety. Meredith also leads Google's Open Research Group which collaborates with open source and academic communities, and the Google Measurement Lab. Meredith is also a Research Scientist at New York University.
- Sarah Bird, @slbird
Sarah received her PhD in Computer Science at the University of California, Berkeley. Now at Facebook, she leads strategic projects to accelerate the adoption and impact of AI research in products. Before joining Facebook, Sarah was an AI systems researcher at Microsoft Research NYC and a technical advisor to Microsoft’s Data Group. Her research includes reinforcement learning systems and AI ethics. Sarah is also one of the researchers behind Microsoft’s Decision Service, one of the first general-purpose reinforcement-learning style cloud systems released.
- Tara Sainath
Tara received her PhD in Electrical Engineering and Computer Science from MIT in 2009, with a focus in acoustic modeling for noise robust speech recognition. After her PhD, Tara spent 5 years at the Speech and Language Algorithms group at IBM T.J. Watson Research Center, before joining Google Research. In addition, Tara is a staff reporter for the IEEE Speech and Language Processing Technical Committee (SLTC) Newsletter. Her research interests are mainly in acoustic modeling, including deep neural networks, sparse representations and adaptation methods. Tara joined RE•WORK back in 2016 when she spoke about her work at the Machine Intelligence Summit in New York.
- Hillary Mason, @hmason
Hillary is a VP of Research at Cloudera. She is also the Founder of Fast Forward Labs, a machine intelligence research company, and was the Data Scientist in Residence at Accel and the Chief Scientist at bitly.
You can hear from some of these influential women that we’ve listed above by joining us at our San Francisco Summit next week where you can learn about their latest work, industry developments and what the future of AI looks like.
On top of RE•WORK’s Women in AI Dinners, we also have a weekly podcast series to further support diversity within the industry. We speak to the leading female minds in AI including CEOs, CTOs, Data Scientists and Industry Professionals from Facebook, McGill University, LinkedIn and many more. If you want to hear from influential women who are helping with the progression of AI and discuss an array of topics then listen here.