Our goal is to develop real unsupervised learning algorithms in the sense that they have real intelligence. We would like to call a family of these algorithms as self-evaluation algorithms.
We used to work on multichannel blind deconvolution and equalization problem which is very fundamental and challenging and has numerous applications in digital communication and wireless communications as well as in brain science. We also have a variety of interests in brain science, especially computational neuroscience and in a variety of topics in information theory, mainly information-theoretic learning.
Currently we are working on machine learning, especially statistical machine learning which included many exciting things such as probabilistic models, graphical models, kernel machines, Bayesian learning, and so on. We are also working on the applications of machine learning, which include brain computer interface, pattern classification, medical imaging, computational hearing/vision, bioinformatics, etc. We would like to keep doing some theoretical work to develop real learning algorithms and will do some application work as well.
Although whole algorithms can not be named as learning algorithms (they are simply adaptive algorithms, they are not really learning), we have called them as learning algorithms. Until we come up with self-evaluation algorithms, we will be excused to call them as learning algorithms.