Emma Brunskill is a distinguished computer scientist whose innovative research bridges artificial intelligence with critical real-world applications in healthcare and education. She currently serves as an associate tenured professor in the Department of Computer Science at Stanford University, holding a courtesy appointment at the Stanford Graduate School of Education. Prior to joining Stanford in March 2017, she established her research program as an assistant professor at Carnegie Mellon University. Her academic journey includes a BS from the University of Washington, an MS from Oxford University, and a PhD in Computer Science from the Massachusetts Institute of Technology in 2009, providing her with a strong interdisciplinary foundation. As an associate director of the Stanford Causal Science Center, she plays a key leadership role in shaping the university's interdisciplinary research initiatives.
Dr. Brunskill's research focuses on developing reinforcement learning systems that learn efficiently from limited data to make robust decisions in high-stakes environments where trial-and-error learning is costly or risky. Her pioneering work has made significant contributions to constraint sampling reinforcement learning techniques that incorporate expert knowledge to accelerate learning processes. She has developed self-optimizing tutoring systems that have demonstrated measurable impact in educational settings, particularly for supporting lower-performing students in mathematics. Her research spans from theoretical advances in sequential decision making to practical implementations tested in real classrooms and healthcare settings. This translational approach has earned her recognition through multiple best paper awards at leading conferences including Educational Data Mining and Reinforcement Learning and Decision Making Symposium.
Beyond her research contributions, Dr. Brunskill actively shapes the field through leadership roles including service on the International Machine Learning Society Board and the Khan Academy Research Advisory Board. She has successfully mentored a remarkable cohort of graduate students and postdoctoral researchers, with many now holding faculty positions at prestigious institutions worldwide or research scientist roles at major technology companies. Emma Brunskill was a 2023 Scholar in Service working with Carnegie Learning to leverage AI to improve middle school math educational outcomes, as confirmed by Stanford Impact Labs. Her ongoing work with Stanford Impact Labs' Empowering Peer Supporters team demonstrates her commitment to developing AI systems that can learn efficiently and make reliable decisions in human-facing applications. Dr. Brunskill's trajectory positions her at the forefront of creating AI that enhances human potential through thoughtful, responsible development and deployment.
