We tackle a wide variety of data-driven applications via investigating optimization and machine learning techniques. Here are some exemplified applications, but we do not restrict ourselves to them.
Low-rank approximation has attracted more and more attention in the community of recommendation. A fundamental assumption in constructing matrix approximations is that the partially observed data matrix is of low-rank. A matrix completion (imputation) gives a tool to predict missing entries for a recommendation system.
Color transfer is a method of transferring color information from a source image to a target image by considering a reference image. This problem is presented in terms of the optimal transportation problem.
Video surveillance (anomaly detection)
Image processing (recovery, basis representation, and denoising)
Network traffic analysis (anomaly traffic detection via inverse problem)
Wireless system architecture design
General-purpose data analysis
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