Research Bio
Bin Yu is a statistician, data scientist, and machine learner whose research focuses on machine learning, artificial intelligence, and the interpretability of complex models. She and her team pioneered veridical (truthful) data science (VDS) for the entire data science life cycle. VDS is based on three core principles of data science: predictability, computability and stability (PCS) for drawing reliable scientific conclusions based on data and domain knowledge. Yu’s work bridges the practice of statistics and machine learning, computation, theory, and applications in neuroscience, genomics, and precision medicine. Her research promotes responsible, transparent, and human-centered AI.
She is CDSS Chancellor’s Distinguished Professor of Statistics and Electrical Engineering and Computer Sciences, and Center for Computational Biology, and Senior Advisor, Simons Institute for the Theory of Computing, all at UC Berkeley. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, she mentors students in veridical data science, statistical learning, AI safety and ethics, and interdisciplinary research.
Research Expertise and Interest
machine learning, trustworthy data science and AI, interdisciplinary research in biomedicine, neuroscience, climate science
In the News
When Data Science Meets Medicine
UC Berkeley Joins NSF-Backed AI Institute for Cybersecurity
UC Berkeley to lead $10M NSF/Simons Foundation program to investigate theoretical underpinnings of deep learning
Getting the right equipment to the right people
Seeking Data Wisdom
Bin Yu’s statistical strategies work hand in hand with intense computation to penetrate storms of data.