Bin Yu
 
 
 
Welcome
The Yu Group's research focuses on practice, algorithm, and theory of statistical machine learning and causal inference. Her group is engaged in interdisciplinary research with scientists from genomics, neuroscience, and precision medicine to extract useful information from empirical evidence based on data and subject domain knowledge. Currently, they are investigating methods/algorithms (and associated statistical inference problems) such as dictionary learning, non-negative matrix factorization (NMF), EM and deep learning (CNNs and LSTMs) in order to augment empirical evidence for decision-making. Their recent algorithms include staNMF for unsupervised learning, iterative Random Forests (iRF) and signed iRF (s-iRF) for discovering predictive and stable high-order interactions in supervised learning, contextual decomposition (CD) and aggregated contexual decomposition (ACD) for phrase or patch importance extraction from an LSTM or a CNN.
 
Chancellor's Professor
 
 Department of Statistics
Department of Electrical Engineering & Computer Science
 
University of California, Berkeley
 
Chan-Zuckerberg Biohub Investigator
 
mail: 367 Evans Hall #3860 • Berkeley, CA 94720 • phone: 510-642-2021 • fax: 510-642-7892
Research
Research supported by grants from NSF, ARO, ONR, and the Center for Science of Information (CSoI), an NSF Science and Technology Center.