USC Melady Lab

Machine Learning for the real world

ABOUT MELADY

The USC Melady Lab develops machine learning and data mining algorithms for solving problems involving data with special structure, including time series, spatiotemporal data, and relational data. We work closely with domain experts to solve challenging problems and make significant impacts in computational biology, social media analysis, climate modeling, health care, and business intelligence.

LATEST NEWS



The review of Yi's work is published on MIT tech review

Dave's paper on Accelerating Active Learning with Transfer Learning was accepted to ICDM 2013.

We built an online tool for visualizing Granger causal networks.

Yan received the 2013 Okawa Foundation Research Award.

Xinran received best technical poster during USC CS annual research review.

Yan gave a tutorial on Causality Analysis from Time Series Data at CIKM 2013.

Dave will be working on mobile health sensing with Scott Sapponas at Microsoft Research Redmond in Summer 2014.

We got three papers accepted to SIGKDD 2014.

Taha and Rose's paper on Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning was accepted to NIPS 2014.

We got two papers accepted to ICDM 2014 (1 regular, 1 short paper).

Dave received the Alfred E. Mann Innovation in Engineering Fellowship.

Yan is offering the class CSCI-567 Machine Learning in the Fall 2014 semester.