Emma Patricia Brunskill is an American computer scientist whose research combines Machine learning with human-computer interaction, focusing on how AI systems affect human-centered applications such as educational software and healthcare. Her work also explores the theory of reinforcement learning in settings where mistakes carry high risks or costs. She is an associate professor of computer science at Stanford University, holds a courtesy appointment in the Stanford Graduate School of Education, and is an affiliate of the King Center on Global Development.
Brunskill's contributions have been recognized with multiple awards, including a National Science Foundation CAREER Award and election as a Fellow of the Association for the Advancement of Artificial Intelligence. Her research has influenced the design of adaptive learning technologies and decision-making algorithms in safety-critical domains.
Education and early career
Brunskill grew up in Seattle and Edmonds, Washington, and entered the University of Washington at age 15. She graduated magna cum laude in 2000 with a bachelor's degree in computer engineering and physics. A Rhodes Scholarship took her to Magdalen College, Oxford, where she earned a master's degree in neuroscience in 2002. After a summer working in Rwanda, she pursued doctoral studies in computer science at the Massachusetts Institute of Technology, completing her Ph.D. in 2009. Her dissertation, "Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains," was supervised by Nicholas Roy.
Following her doctorate, Brunskill worked as an NSF Postdoctoral Research Fellow at the University of California, Berkeley. In 2011, she joined Carnegie Mellon University as an assistant professor of computer science. She moved to Stanford University in 2017, where she became an associate professor and affiliated with the Stanford AI Lab.
Research contributions
Brunskill's research sits at the intersection of Artificial intelligence, Machine learning, and human-computer interaction. A central theme is developing reinforcement learning algorithms that can operate effectively in real-world settings where exploration is costly or dangerous, such as personalized tutoring or clinical decision support. Her work often involves creating compact parametric models to handle high-dimensional, uncertain domains, enabling more efficient sequential decision making.
In education, Brunskill has investigated how AI systems can adapt to individual learners, using data from student interactions to improve instructional strategies. Her approach integrates insights from cognitive science and educational psychology with advances in Deep learning and Neural network methods. In healthcare, she has explored algorithms that balance the need for learning with the imperative to avoid harmful mistakes, a challenge that connects to broader questions in Curriculum Learning and safe exploration.
Brunskill has also contributed to the theoretical foundations of reinforcement learning, particularly in understanding how to design algorithms that are robust to uncertainty and limited data. Her work has implications for Large language model-based tutoring systems and other AI-driven educational tools.
Recognition and awards
Brunskill received the National Science Foundation CAREER Award in 2014 and the Office of Naval Research Young Investigator Award in 2015. In 2020, she was one of two alumni of the University of Washington's Paul G. Allen School of Computer Science and Engineering honored by the school's Alumni Impact Awards. In 2025, she was elected as a Fellow of the Association for the Advancement of Artificial Intelligence, cited "for significant contributions to the field of reinforcement learning, and applications for societal benefit, in particular AI for education."
Impact and broader influence
Brunskill's research has helped bridge the gap between theoretical reinforcement learning and practical applications in education and healthcare. Her work has informed the development of adaptive learning platforms and has been influential in shaping how AI systems are designed to interact with humans in high-stakes environments. As an affiliate of the King Center on Global Development, she has also explored how AI can address global challenges, including improving access to quality education in resource-limited settings.
Her contributions extend to mentoring and building community in the AI research field, particularly through her roles at Stanford and her involvement in academic conferences and workshops focused on AI for social good.