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.
－《韩非子 - 五蠹》
"There was a farmer of Sung who tilled the land, and in his field was a stump. One day a rabbit, racing across the field, bumped into the stump, broke its neck, and died. Thereupon the farmer laid aside his plow and took up watch beside the stump, hoping that he would get another rabbit in the same way. But he got no more rabbits, and instead became the laughing stock of Sung."
- Hanfeizi Book 49 (233 BC)