Meet These Incredible Women Advancing A.I. Research

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.

 

Fei-Fei Li

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.”

 

Daphne Koller

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%.

 

Cynthia Breazeal

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.”

 

Latanya Sweeney

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.

 

Andrea Frome

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

Co-Founder, Affectiva

“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.”

 

Carol Reiley

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.

 

Hua Wu

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.”

 

Angelica Lim

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.

 

Daniela Rus

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.”