Dr. Yi Lu Murphey is an Associate Dean for Graduate Education and Research, a Professor of the ECE(Electrical and Computer Engineering) department and the director of the Intelligent Systems Lab at the University of Michigan, Dearborn. She received a M.S. degree in computer science from Wayne State University, Detroit, Michigan, in 1983, and a Ph.D degree with a major in Computer Engineering and a minor in Control Engineering from the University of Michigan, Ann Arbor, Michigan, in 1989. Her current research interests are in the areas of machine learning, pattern recognition, computer vision and intelligent systems with applications to automated and connected vehicles, optimal vehicle power management, data analytics, and robotic vision systems. She has authored over 130 publications in refereed journals and conference proceedings. She is an editor for the Journal of Pattern Recognition, a senior life member of AAAI and a fellow of IEEE.
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.
My research focuses on the empirical and theoretical analysis of social and information networks. I am particularly interested in understanding the mechanisms involved in network evolution, information diffusion, and interactions among people on the Web and in complex organizations.
Jieping Ye, PhD, is Associate Professor of Computational Medicine and Bioinformatics in the Medical School at the University of Michigan, Ann Arbor.
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.
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.