Research Area

We develop algorithms and theories for statistical machine learning and graphical models, with applications to search and mining, biomedical data analysis and bioinformatics, and user interface. Of particular methods that we are working on are Bayesian learning, convex optimization, dimensionality reduction, kernel machines, manifold learning, matrix and tensor factorizations, probabilistic graphical models, semi-supervised learning, transfer learning.

Nonparametric Bayesian Learning

Nonparametric methods are very flexible models for inferring non-linear functions, or solving density estimation problems. Specially, in the Bayesian approach, Gaussian processes and Dirichlet processes ...

Matrix factorization

Component analysis (e.g. PCA, ICA, FLDA, NMF) have been successfully applied in numerous visual, graphics and signal processing tasks over the last two decades. Recently, these methods are generalized to multilinear methods ...

Multi-modal Learning

Real-world data including image, documents may have multiple representation, and each of the representations may be called a 'view'. Multi-view learning refers to the methods that utilizes multiple representations of data. In multi-view learning ...

Brain-computer Interface

Brain computer interface (BCI) is a system that is designed to translate a subject's intention or mind into a control signal for various devices. Electroencephalogram (EEG), which captures electrical potentials ...

Reading Group

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