Learning Temporal Causal Models for Emerging Applications with Time-Series Data

Sunday, February 20, 2011 - 11:46am


Monday, March 7, 2011
11:00 AM – 12:00 PM
Computer Science Conference Room, Harold Frank Hall Rm. 1132

HOST: Xifeng Yan

Computer Science, University of Southern California

Title: Learning Temporal Causal Models for Emerging Applications with Time-Series Data


Many emerging applications of machine learning, such as social media analysis, climate modeling, and computational biology, involve data with inherent structures. In this talk, I will examine how to develop effective machine learning algorithms to uncover the temporal dependencies from time-series data. Specifically, I will introduce Granger temporal models that allow us to model causal relationships from time series data by appealing Granger causality with success in computational biology, climate science and oil-drilling monitoring applications.


Yan Liu is an assistant professor in Computer Science Department at University of Southern California from 2010. Before that, she was a Research Staff Member at IBM Research from 2006. She received her M.Sc and Ph.D. degree from Carnegie Mellon University in 2004 and 2006. Her research interest includes developing scalable machine learning and data mining algorithms with applications to social media analysis, computational biology, climate modeling and business analytics. She has received several awards, including 2007 ACM Dissertation Award Honorable Mention, best application paper award in SDM 2007, and winner of several data mining competitions, including KDD Cup 2007, 2008, 2009 and INFORMS data mining competition 2008. She has published over 40 referred articles and served as a program committee of SIGKDD, ICML, NIPS, CIKM, SIGIR, ICDM, AAAI, COLING, EMNLP and co-chair of workshops in KDD and ICDM.