You already know that artificial intelligence is eating the world, transforming virtually every industry and function. But you might not have met the brilliant AI researchers and technologists driving the edge of innovation.
Incredible breakthroughs occur when talented and diverse thinkers collaborate, pooling together unique backgrounds, disciplines, expertise, and perspectives. Holistic and inclusive thinking is even more important in the field of AI, where our inventions have pervasive and exponential impact.
This list of 20+ leading women in AI research is not comprehensive. Far more talented people contribute to the field than we can quickly summarize in a single article. All of the women featured here overcame personal and professional challenges to achieve incredible impact and become leaders and role models for the industry.
We are proud to tell you their stories.
Chief Scientist of Artificial Intelligence & Machine Learning, Google Cloud
“We all have a responsibility to make sure everyone – including companies, governments and researchers – develop AI with diversity in mind,” emphasizes Fei-Fei Li.
A renowned academic in computer vision, Li recently joined Google Cloud as Chief Scientist of Artificial Intelligence & Machine Learning to advance her mission of “democratizing AI”. She continues to act as an Associate Professor at Stanford, where she directs both the Stanford AI Lab and Stanford Vision Lab. Since obtaining a B.A. in Physics from Princeton and a PhD in Electrical Engineering from Caltech, Li has published over 150 scientific papers in top-tier journals and conferences and built ImageNet, a 15 million image dataset that contributed to the latest developments in deep learning and AI.
Li points out that locking up talent and knowledge in academia and giant companies damages computing diversity, reduces creativity and innovation, and exposes the marginalized to injustice and unfairness. Her non-profit, AI4ALL, supports K-12 education programs for underrepresented groups in AI.
“Technology could benefit or hurt people, so the usage of tech is the responsibility of humanity as a whole, not just the discoverer. I am a person before I’m an AI technologist.”
Chief Computing Officer, Calico Labs
During her 18 years as a Professor of Computer Science at Stanford, Daphne Koller authored over 200 publications in top scientific journals and won an innumerate number of awards for academic breakthroughs and excellence in education. She went on to co-found Coursera, the world’s largest online education platform, and now serves as Chief Computing Officer at Calico Labs, an Alphabet (Google) R&D company studying the biology of aging and developing interventions for longer and healthier lives.
Among her cross-disciplinary achievements, Koller is most proud of educating students who have gone on to make their own incredible contributions, including the millions who enrolled in AI, machine learning, and data science courses on Coursera. Of learners who completed courses, 29% reported tangible benefits, such as starting new careers or businesses. Importantly, for disadvantaged students from emerging economies or low socioeconomic backgrounds, the number jumps to 48%.
Founder & Chief Scientist, Jibo
A world renowned pioneer in social robotics, Cynthia Breazeal splits her time as an Associate Professor at MIT, where she received her PhD and founded the Personal Robots Group, and Founder and Chief Scientist of Jibo, a personal robotics company with over $85 million in funding.
While Breazeal’s work has won numerous academic awards, industry accolades, and media attention, she had to fight early skepticism in the 1990s from other experts in robotics and AI. At the time, robots were seen as physical and industrial tools, not social or emotional companions. Her first social robot, Kismet, was unfairly called out in popular press as “useless”.
Breazeal bucked the trend with a very different vision: “I wanted to create robots with social and emotional intelligence that could work in collaborative partnership with people. In 2-5 years, I see social robots helping families with things that really matter, like education, health, eldercare, entertainment, and companionship.”
She hopes her work and influence will inspire others to create robots “not only with smarts, but with heart, too.”
Professor of Government and Technology, Harvard University
As a Professor of Government and Technology at Harvard and Director of Harvard’s Data Privacy Lab, Latanya Sweeney tackles challenges of security, privacy, and bias in personal data and machine learning algorithms.
Sweeney’s research has exposed discrimination in online advertising, where internet searches of names “racially associated” with the black community are 25% more likely to yield sponsored ads suggesting that the person has a criminal record, regardless of the truth. In her role as Editor-In-Chief of Technology Science, she reported that SAT test prep services charge zip codes with high proportions of Asian residents nearly double the average price, regardless of their actual income. Price discrimination based on race, religion, nationality, or gender is illegal in the United States, but enforcement of the law is challenging in online commerce where differential pricing is wrapped up in opaque algorithms.
Prior to her current role, Sweeney was CTO of the Federal Trade Commission. She completed her undergraduate studies in computer science at Harvard and was the first black woman to receive a PhD in Computer Science from MIT.
Director of Research, Clarifai
Andrea Frome didn’t start her career intending to become a top AI researcher. Originally an environmental scientist, she fell in love with the data and modeling aspects of her work, which inspired her to switch gears and pursue a PhD in Computer Vision and Machine Learning at Berkeley. She then joined Google, where she published seminal research papers on multi-modal visual classification systems and launched Google Street View.
“I’ve often found greater satisfaction in solving problems with impact reaching beyond the academic community,” she explains. “In the case of Street View, we needed to blur faces and license plates for privacy protection. Getting the detection accuracy high enough was a hard real-world problem and Street View couldn’t be launched unless we solved it.”
Frome is currently Director of Research at Clarifai, a leading computer vision company. Her ultimate goal is to enable computers to understand visual input the way humans do and make accurate predictions about the world around them.
Rana el Kaliouby
“The field of AI has traditionally been focused on computational intelligence, not on social or emotional intelligence,” explains Rana el Kaliouby. “Yet being deficient in emotional intelligence (EQ) can be a great disadvantage in society.”
El Kaliouby was born in Cairo, Egypt and grew up in the Middle East. When she started her PhD in Computer Science at Cambridge University in England, few did research in artificial emotional intelligence. Through continued passion, advocacy, and demonstrable technical progress, el Kaliouby defined the field of “emotion AI” and co-founded Affectiva, where she leads as CEO. Affectiva’s technology has proven transformative for industries like automotive, market research, robotics, education, and gaming, but also for use cases like teaching autistic children emotion recognition and nonverbal social cues. One mother broke down in tears when her child, using Affectiva-powered Google Glasses, learned to make true eye contact with her for the first time.
“3-5 years from now, our devices will be emotion-aware,” predicts el Kaliouby. “You won’t remember what it was like when your technology didn’t recognize when you are sad or angry.”
Co-Founder & President, Drive.ai
Carol Reiley didn’t start programming until her first day of college as a freshman engineering major. Pitted against students who’d been coding since they were ten, she found the experience “tremendously intimidating” and “almost quit several times”. Luckily, she not only persisted but thrived, going on to pursue a master’s and PhD in Computer Science and Robotics at Johns Hopkins.
“Back in the 1800s, companies would hire a VP of Electricity,” Reiley remembers. “Electricity was this brand-new concept everyone was excited about, but nobody knew how exactly it would impact the world. We see AI in the same way now.” Challenges and ambiguities aside, Reiley’s mission since childhood has been to impact the world through engineering.
She’s now Co-Founder and President of Drive.ai, which formed out of Stanford University’s AI Lab and builds deep learning software for self-driving cars. Despite competition from deep-pocketed tech giants and auto industry skepticism, Reiley and her team raised a $12M Series A, grew the company to 60+, and released several autonomous vehicles on the road.
Technical Chief, Baidu Natural Language Processing (NLP) Team
In her 7 years at Baidu, technical chief Hua Wu has been responsible for a number of breakthroughs in natural language processing (NLP), dialogue systems, and neural machine translation (NMT). Her proposal for a multi-task learning framework for NMT was hailed by the New York Times as “pathbreaking” and successfully deployed at scale to hundreds of millions of users using Baidu’s translation products. She also built the technology behind Duer, Baidu’s conversational AI which powers home assistants and smart IoT devices. Wu received her PhD from the Chinese Academy of Sciences and co-chairs leading academic AI conferences such as ACL and IJCAI.
When Wu first started her research, deep learning had made material progress in computer vision and speech, but not yet in NLP. Many established experts were skeptical that deep learning could improve machine translation, but Wu and her team not only proved the utility but shipped in less than 6 months a working product that processes 100 million translations a day.
“I’m proud of the vision, tenacity and speed of my team,” she beams. “Our improved translation breaks language barriers for people and helps them learn something new.”
Software Development Manager, R&D, Softbank Robotics
10 years ago, way before deep learning was cool, Angelica Lim used Yann LeCun’s convolutional neural networks to break Hotmail’s CAPTCHA system. She even did it in the recursive programming language LISP, but never published her results since neural networks weren’t in vogue then.
During her masters and PhD at Kyoto University, Lim combined computer science with neuroscience and cultural development psychology to build a robot that “feels”. As a pioneer in “developmental robotics”, which models human-style learning in machines, Lim explains that toddlers link names of emotions to specific sets of physiological and psychological states as well as physical expressions. Learning for both humans and robots is heavily influenced by caregivers and culture.
Lim is currently a Robotics Software Development Manager in R&D at Softbank Robotics, creators of the humanoid robot Pepper. She’s given a number of TED talks on designing and co-existing with emotional and empathetic robots.
Director, MIT Computer Science & Artificial Intelligence Laboratory (CSAIL)
Daniela Rus is a Professor of Electrical Engineering and Computer Science at MIT, Director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), and head of CSAIL’s Distributed Robotics Lab. She previously founded the Dartmouth Robotics Lab and is known for her pioneering work in self-reconfiguring robots which adapt to different environments by changing their internal structure.
“Our recent 3D-printed soft robots are safer, cheaper and more resilient than hard-bodied robots created through traditional manufacturing,” she explains. The agile structures of soft robots enable them to easily change direction and squeeze into tight spots. Being able to 3D print them also democratizes manufacturing.
“Using simple household materials like paper and plastic, we can produce functional robots that practically walk right out of the printer.”
Ayse Naz Erkan
Staff Data Scientist, Twitter
Originally from Istanbul, Turkey, Ayse Naz Erkan moved to the US in 2004 for a PhD in Computer Science at the Courant Institute of NYU. She researched deep learning applications for off-road autonomous robot navigation in Yann LeCun’s lab and studied semi-supervised learning at the Max Planck Institute For Biological Cybernetics before joining a tech startup as an early engineer.
Erkan describes her startup days as “incredibly life-changing”, transforming her into a better problem solver and pragmatic technologist. She now leads the Content Understanding and Applied Deep Learning team at Twitter, which acquired the company 5.5 years ago, and has worked to make the social network a safer place.
“Working on hate speech and abuse with Twitter data was quite exciting,” reflects Erkan, “Especially witnessing first-hand how machine learning is impacting public communication design.”
Research Scientist, Google DeepMind
Jane Wang started out as an applied physicist modeling the complex network dynamics of memory systems in the brain before moving into experimental cognitive neuroscience as a postdoc at Northwestern. Since j
oining DeepMind two years ago, her non-machine learning background has equipped her with a unique set of tools and perspectives for tackling the hardest AI problems. “It’s exhilarating to formulate theories of human brain function as powerful deep reinforcement learning models that can solve similarly complex tasks,” she shares.
Though Wang has been successful without a formal AI background, she’s concerned the steep learning curve and hypercompetitive atmosphere of AI research can discourage diverse participation. “Although competitiveness drives the field forward, it also discourages those who wish to work in more inclusive, cooperative environments,” she warns. Wang is on a steering committee at DeepMind to increase diversity in AI and is encouraged by the AI community’s openness for sharing research and driving collective progress.
Machine Learning Engineer, Thumbtack
Carolina Galleguillos was born in Santiago, Chile. After graduating with honors and an Engineering and Computer Science degree from University of Chile, she won a government scholarship for a Silicon Valley internship which eventually led her to complete her PhD in Computer Science at UCSD. Throughout her academic career, she published research at major computer vision conferences and was awarded IGERT NSF fellowships in 2007 and 2008.
Galleguillos has developed computer vision and machine learning algorithms for giants like Google, Hewlett-Packard, Honda, and Thumbtack, but she’s particularly proud of the scrappy AI team she built and trained at SET Media. Despite very limited resources, her group shipped production machine learning systems that were critical to the company’s acquisition by Conversant in 2014.
Visiting Research Scientist, Facebook AI Research
Devi Parikh is an Assistant Professor in the School of Interactive Computing at Georgia Tech and a Visiting Researcher at Facebook AI Research (FAIR). After receiving her masters and PhD in Electrical and Computer Engineering from Carnegie Mellon, she’s held multiple visiting positions at top research labs and won accolades such as the 2017 IJCAI Computers and Thought award, considered “the premier award for AI researchers under the age of 35”.
Parikh’s most proud of her research work in Visual Question Answering (VQA) which lies at the intersection of computer vision and natural language processing (NLP). “Through making our large datasets and systems publicly available, we’ve enabled research groups around the world to make significant progress on building machines that can automatically answer questions about visual content,” she highlights. Such technology can aid the visually impaired and transmit information on low-bandwidth networks that can’t support images.
Advances in VQA also improve existing product experiences. “We’ll see more and more conversational agents – be it personal assistants or chatbots – that can see, or augmented reality experiences that are visually intelligent.”
Professor of Computer Science, University of Maryland, Baltimore County (UMBC)
Marie desJardins has always been driven by broad, big-picture questions in AI rather than narrow technical applications. For her PhD dissertation at Berkeley, she worked on “goal-driven machine learning” where she designed methods an intelligent agent can use to figure out what and how to learn. As an Associate Dean and Professor at University of Maryland, Baltimore County (UMBC), desJardins has published over 120 scientific papers and won accolades for her teaching, but is equally proud of work she’s done with graduate students on self-organization and trust in multiagent systems.
When desJardins started her career, the AI and computing industry attracted more diverse, multi-disciplinary talent. Over time, she observed that conferences are “increasingly dominated with papers that focused almost exclusively on one subproblem (supervised classification learning) and much less welcoming of work in other subareas (active learning, goal-directed learning, applied learning, cognitive learning, etc),” which she is worried will exacerbate the diversity gap in AI.
“We are already seeing a reconsideration of more symbolic, representation-based approaches,” desJardins observes. “Ultimately I think that we will build more and more bridges between numerical approaches and symbolic approaches, and create layered architectures that take advantage of both.”
Since getting her PhD in Mathematics from Duke, Rachel Thomas has worked as a quant, data scientist & backend engineer at Uber, and professor in University of San Francisco’s (USF) Masters of Analytics program. She’s currently a researcher-in-residence at USF’s Data Institute and co-founded Fast.ai which makes practical deep learning education accessible globally. Thomas’ students have leveraged their knowledge to reduce farmer suicides in India, assist the visually impaired, and treat Parkinson’s disease.
When Thomas first started researching deep neural networks a few years ago, virtually no educational resources existed online. “It seemed like everyone in the field had done their PhD with the same four advisors and nobody was sharing the practical, useful info,” she observed. As a solution, she co-produced a free Practical Deep Learning For Coders course intended to ramp anyone with reasonable coding skills up on applied neural network approaches. Thomas’ initiative has been successful in enabling more women, people of color, international students, and the economically disadvantaged to participate in AI research and engineering.
Assistant Professor, Johns Hopkins University
Vast amounts of health information are collected digitally, yet largely underutilized for extracting insights to improve healthcare. Suchi Saria, Assistant Professor at Johns Hopkins University, believes computational modeling of data from sensor platforms and electronic medical records presents “a tremendous opportunity for high impact work.”
Prior to Johns Hopkins, Saria did her PhD at Stanford with Dr. Daphne Koller and was an NSF Computing Innovation Fellow at Harvard. She was initially convinced she would not like biology or medicine, but became hooked after studying disease prevention in infants using continuously collected physiological data. With her multi-disciplinary expertise, Saria has published highly regarded scientific papers on disease trajectory modeling, predictive methods for care targeting, clinical decision support (CDS) systems, and individualized treatment approaches.
Saria passionately encourages upcoming researchers to pick important problems to work on and delve deeper into their complexity and constraints.
Distinguished Engineer & Master Inventor, IBM Watson
Very few earn the title of “Distinguished Engineer and Master Inventor” at IBM, but Rama Akkiraju’s contributions warrant the distinction. She leads the mission of “People Insights” at IBM Watson and develops technologies that infer personalities, emotions, tone, attitudes, and intentions from social media data using linguistic and machine learning techniques. Akkiraju helmed the teams responsible for many of Watson cognitive services, including Tone Analyzer.
To tackle this challenging space, Akkiraju’s teams leverage multiple disciplines including AI, psychology, sociology, decision theory and consumer behavior. “Bots that really understand people can bridge the shortage of customer support agents, guidance counselors, and health coaches,” she points out. “These are all areas where our work can make a meaningful difference in people’s everyday lives.”
Jackie Hunter has always been fascinated by big data’s potential to transform biotech, but saw first-hand as a senior executive in R&D at GlaxoSmithKline how little of the data is actually mined to produce insights for better medicine. Now, as CEO of BenevolentBio, the bioscience arm of BenevolentAI, Hunter combines her extensive academic and industry experience to accelerate drug discovery and development with artificial intelligence.
“In drug discovery and healthcare, I believe the next 5 years are going to see more transformation than the previous 50,” she predicts, but also warns against pharmaceutical companies adopting AI in a piecemeal fashion. “Companies that develop and implement an integrated digital and AI strategy across the whole value chain will be those that will succeed in the next 5-10 years.”
Senior Director, Data Science, Salesforce Einstein
Since receiving her PhD in Computer Science from Stanford, Shubha Nabar has built data products and data science teams at Microsoft, LinkedIn, and now Salesforce. As a senior director of data science on Einstein, she and her team make AI accessible to business users by infusing intelligent functionality across all Salesforce products.
Building AI solutions for hundreds of thousands of enterprises across a huge array of use cases is technically challenging. Nabar’s novel approach is to build a “meta” machine learning framework that automates the building of entire machine learning pipelines. “Leading the team through these challenges has been incredibly rewarding because we’re building something completely unprecedented,” she shares.
When machine learning is democratized, Nabar cautions that “with this ubiquitousness, we need to emphasize ethical implications of AI and building fair and accountable algorithms that do not propagate bad biases that often exist in real world data.”
PhD Candidate, Stanford University
As a teenager, Timnit Gebru left Ethiopia for the United States. She thrived in her new country, where she enrolled at Stanford University for a bachelor’s, master’s, and PhD in Electrical Engineering, landed a prestigious engineering job at Apple, and co-founded a startup. Studying computer vision under Dr. Fei-Fei Li, Gebru authored several notable papers in her research area of mining large scale datasets for sociological insights. Her recent work using machine learning methods to extrapolate census data from Google Street View images was lauded by The Economist.
Gebru actively works to boost diversity and inclusion in the field of AI. After noticing she was the only black woman at a major AI conference, she co-founded the social community Black In AI to drive connection and participation in AI research. Gebru also returned to Ethiopia to co-teach AddisCoder, a programming bootcamp, to a diverse range of young students and help them win admission to Ivy League universities.
Since AI affects all aspects of society, even being used to manipulate elections and identify criminals, Gebru cautions that “AI researchers should not be silent regarding the repercussions of their work.” Only when technology creators tend to inclusion will the exponential benefits of artificial intelligence positively impact all.
This feature on Women in AI was originally developed for Forbes by TOPBOTS Editor-In-Chief Mariya Yao.