We are interested in developing machine learning theories, algorithms, and applications to problems in science, engineering and computing. We use the tools of statistical inference and large-scale computing to deal with uncertainty and information in various domains, including text mining, image & video processing, network analysis, and neuroscience. Our recent projects include deep learning, scalable (regularized) Bayesian inference, topic models, learning from crowds, and their applications in various domains.
We actively seek to collaborate with other groups around the world. If you are interested in finding out more about our research, please visit our publication page.