1) foundations of statistical learning, including theory and algorithms for estimating time/space varying-coefficient models, sparse structured input/output models, and nonparametric Bayesian models;
2) computational and statistical analysis of gene regulation, genetic variation, and disease associations; and
3) large-scale systems for machine learning.Professor Xing has published over 200 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics (AOAS), the Journal of American Statistical Association (JASA), the IEEE Transaction of Pattern Analysis and Machine Intelligence (PAMI), the PLoS Journal of Computational Biology, and an Action Editor of the Machine Learning Journal (MLJ), the Journal of Machine Learning Research (JMLR). He is a member of the DARPA Information Science and Technology (ISAT) Advisory Group, a recipient of the NSF Career Award, the Sloan Fellowship, the United States Air Force Young Investigator Award, the IBM Open Collaborative Research Award, and best paper awards in a number of premier conferences including UAI, ACL, EMNLP, SDM, ISMB. He is the Program Chair of ICML 2014.