Lu Wei

By | | No Comments

Lu Wei, DSc,  is Assistant Professor in the Department of Electrical and Computer Engineering at the University of Michigan, Dearborn.

Prof. Wei studies the analytical properties of interacting particle systems relevant to both classical and quantum information theory.



Jieping Ye

By | | No Comments

The Ye Lab has been conducting fundamental research in machine learning and data mining, developing computational methods for biomedical data analysis, and building informatics software. We have developed novel machine learning algorithms for feature extraction from high-dimensional data, sparse learning, multi-task learning, transfer learning, active learning, multi-label classification, and matrix completion. We have developed the SLEP (Sparse Learning with Efficient Projections) package, which includes implementations of large-scale sparse learning models, and the MALSAR (Multi-tAsk Learning via StructurAl Regularization) package, which includes implementations of state-of-the-art multi-task learning models. SLEP achieves state-of-the-art performance for many sparse learning models, and it has become one of the most popular sparse learning software packages. With close collaboration with researchers at the biomedical field, we have successfully applied these methods for analyzing biomedical data, including clinical image data and genotype data.


Shuheng Zhou

By | | No Comments

In the “Big Data” era, data sets are often very large yet incomplete, high dimensional, and complex in nature. Analyzing and deriving critically useful information from such data poses a great challenge to today’s researchers and practitioners. The overall goal of the research agenda of my group is to develop new theoretical frameworks and algorithms for analyzing such large, complex and spatio-temporal data despite the overwhelming presence of missing values and large additive errors. We propose to develop parametric and nonparametric models and methods for (i) handling challenging situations with additive and multiplicative errors, including missing values, in observed variables; (ii) estimating dynamic time varying correlation and graphical structures; (iii) addressing fundamental challenges in “Big Data” such as data reduction, aggregation, interpretation and scale. We expect to uncover the complex structures and the associated conditional independence relationships from observation data with an ensemble of newly designed estimators. Our methods are applicable to many application domains such as neuroscience, geoscience and spatio-temporal modeling, genomics, and network data analysis.