Bin Yu is the head of the Yu Group at Berkeley, which consists of 15-20 students and postdocs from Statistics and Electrical Engineering and Computer Science at UC Berkeley. Yu was formally trained as a statistician, but her research interests and achievements extend beyond the realm of statistics. Together with her group, Yu's work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of my many collaborators in neuroscience, genomics and precision medicine. Her lab also develop relevant theory to understand random forests and deep learning for insight into and guidance for practice.
Her lab has developed the PCS framework for veridical data science (or responsible, reliable, and transparent data analysis and decision-making). PCS stands for predictability, computability and stability, and it unifies, streamlines, and expands on ideas and best practices of machine learning and statistics.
In order to augment empirical evidence for decision-making, they are investigating statistical machine learning 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 interpretation of Deep Neural Networks (DNNs).