Eric P. Xing
Assistant Professor
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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.

Statistical Machine Learning , emphasizing theory and algorithms for learning complex probabilistic models, learning with prior knowledge, and reasoning and decision-making in open, evolving and uncertain possible worlds. We focus on,

1) Variational inference and learning theory, and development of turn-key variational inference engines

2) Nonparametric Bayesian analysis, infinite mixture models, algorithms and applications of Bayesian nonparametrics for data mining and object/topic/event tracking in open, evolving possible worlds

3) Statistical modeling and analysis of network and relational data, especially reverse engineering and meta-analysis of temporally evolving social and biological networks

4) Statistical machine learning models and algorithms for image/text/relational information retrieval

5) Probabilistic and optimization-theoretic methods for semi-unsupervised learning and distance metric learning.

Computational Biology , with an emphasis on developing formal models and algorithms that address problems of practical biological and medical concerns, such as,

1) Modeling evolution of gene expression, cis-regulatory code and transcriptional regulatory network in metazoan organisms

2) Modeling genome-microenvironment interactions in cancer development and embryogenesis via joint analysis of genomic, proteomic, cytogenetic and pathway signaling data

3) Statistical inference on genetic fingerprints, pedigrees, and their associations to diseases and other complex traits; application to clinical diagnosis and forensic analysis

4) Modeling substitution, recombination, selection and genome rearrangement for comparative genomic analysis

5) Biological image and text mining