Machine Learning and Data Mining
Subareas: Data Integration, Knowledge Discovery, Machine Learning, Scientific Data Management, Visual Analytics
Data Mining: Our group has a long history of developing data mining methods and has successfully applied them to solve problems in many other disciplines. Our interests include clustering and subspace clustering in high dimensional data, matrix factorization, graph mining and classification, efficient methods for large scale statistical tests.
Machine Learning: The problems we study combine vast amounts and disparate types of measurements with equally complex prior knowledge, posing unique challenges for machine learning. Our interests include both modeling paradigms, such as Bayesian nonparametric methods, and inference methodologies, such as MCMC, variational methods and convex optimization.