Bin Yu

Bin Yu is CDSS Chancellor's Distinguished Professor in Statistics, EECS, and Center for Computational Biology, and Senior Advisor at Simons Institute for the Theory of Computing, at UC Berkeley. Her research advances the practice and theory of machine learning and AI, emphasizing trustworthy AI and interdisciplinary applications in neuroscience, genomics, and precision medicine. She pioneered the veridical data science framework for reproducible scientific conclusions and has developed effective interpretable ML/DL methods for understanding compositionality, including iterative Random Forests (iRF), stability-driven NMF, Adaptive Wavelet Distillation (AWD), Contextual Decomposition for Transformers (CD-T), and SPEX/ProxySPEX. Yu is a member of the US National Academy of Sciences and the American Academy of Arts and Sciences. She was a Guggenheim Fellow and a Chan-Zuckerberg Biohub Investigator. She has received some of the most prestigious awards in statistics and ML including the Tukey, Wald, COPSS DAAL (formerly Fisher Lecture), and Breiman Lectures. She recently appeared on SANO Genetics Podcast for her ground-breaking work on human functional genomics. Her past research covers a wide range of research on empirical process theory, information theory (MDL), MCMC methods, signal processing, machine learning, high dimensional data inference, and interdisciplinary data problems in remote sensing, signal processing, network tomography and audio compression.