Bin Yu
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 based on data and domain knowledge. In order to augment empirical evidence for decision-making, 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), and heterogeneous effect estimation in randomized experiments (X-learner). 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 contextual 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 supported by grants from NSF, ARO, ONR, and the Center for Science of Information (CSoI), an NSF Science and Technology Center.