The Yu Group, led by Professor Bin Yu, works 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 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. Their recent work on forecasting and curating COVID-19 deaths can be viewed at covidseverity.com.
