Professional short bio: Dr. Eric Xing is an associate professor in the School of Computer Science at Carnegie Mellon University. His principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional and dynamic possible worlds; and for building quantitative models and predictive understandings of biological systems. Professor Xing received a Ph.D. in Molecular Biology from Rutgers University, and another Ph.D. in Computer Science from UC Berkeley. His current work involves, 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) application of statistical learning in social networks, data mining, vision. Professor Xing has published over 90 peer-reviewed papers, and is an associate editor of the Annals of Applied Statistics, the PLoS Journal of Computational Biology, and a member of the editorial board of the Machine Learning journal. He is a recipient of the NSF Career Award, the Alfred P. Sloan Research Fellowship in Computer Science, and the United States Air Force Young Investigator Award.

Research synopsis: My principal research interests lie in the development of machine learning and statistical methodology; especially for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds; and for building quantitative models and predictive understandings of the evolutionary mechanism, regulatory circuitry, and developmental processes of biological systems. Currently the following themes are studied in my group:

• Foundations of Statistical Learning, including : 1) Theory and algorithms for estimating time/space varying-coefficient models with evolving strcutures; 2) Learning sparse structured input/output models in very high-dimensional space; 3) Nonparametric techniques for infinite-dimensional models; 4) Active learning.
• Computational Biology, including: 1) Comparative genomic analysis of regulatory evolution; 2) Systems biology investigation of time-varying gene regulation circuity; 3) Statistical genetic analysis of population variation, demography and evolution; 4) Structured inference of genome-transcriptome-phenome association in complex diseases.
• Applications of Statistical Learning, in social/bio network analysis, text/image data mining, computer vision, and machine translation.