Eric P. Xing

The major theme of Professor Xing's research lies in the development of machine learning and statistical methodology; especially for building quantitative models and predictive understandings of the evolutionary mechanism, regulatory circuitry, and developmental processes of biological systems; and for building intelligent systems for a wide range of applications in vision, IR and NLP that involves computational learning and reasoning under uncertainty.

Foundations of Statistical Learning, including theory and algorithms for: 1) Time/space varying-coefficient models with evolving structures; 2) Sparse structured input/output models in high-dimensional problems; 3) Nonparametric Bayesian techniques for infinite-dimensional models; 4) RKHS embedding, nonparametric inference, and spectral methods for graphical models; 5) Distributed and online algorithms for optimization, approximate inference, and sampling on massive data.

Large-scale Information & Intelligent System: 1) Development of scalable parallel architecture, protocol, programming interface, generic algorithms and models, for Big Learning; 2) Multi-view latent space models, topics models, and sparse coding for image/text/relational data mining; 3) Evolving structure, stable metrics, and prediction for dynamic social networks, goal-driven network design and optimization; 4) Web-scale image understanding, search, prediction, and storyline synthesis; 5) Information visualization, indexing and storage, web/mobile app development.

Computational Biology: 1) Understanding genome-microenvironment interactions in cancer and embryogenesis via joint analysis of genomic, proteomic, and pathway signaling data; 2) Genetic analysis of population variation, demography and evolution; 3) Statistical inference of genome-transcriptome-phenome association in complex diseases; 4) Personalized diagnosis and treatment of spectrum diseases via next generation sequencing and computational "omic" analysis; 5) Biological image and text mining.