Login
Computer Science @ Rochester
Monday, February 04, 2013
10:45 AM
CSB 209
Xi Chen
Carnegie Mellon University
Learning from Big Data: Scalability & Structures
The development of modern technology has enabled collecting data of unprecedented size and complexity. Examples include web text data, microarray & proteomics, climatological data, social network data, to name few. To learn from these large-scal and complex data, traditional machine learning techniques either suffer from unaffordable computational cost or are unable to model the complex intrinsic structures latent in data.

To facilitate big data analysis, we need both advanced computational methods to address scalability; as well as new statistical models to extract hidden structures from the data. In this talk, I present two research threads to address challenges from both computational and statistical aspects in modern data analysis: (1) An uniformly-optimal stochastic first-order method for large-scale online prediction and its implementation in a distributed environment. (2) A computationally efficient method for predicting dynamic graphical models from complex data. I will also talk about the applications of the proposed methods, such as text mining and climate data analysis.