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Dr. Eric Xing is a Professor of Machine Learning in the School of Computer Science at Carnegie Mellon University, and the founding director of the CMU/UPMC Center for Machine Learning and Health.
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, multimodal, and dynamic possible worlds in social and 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) large-scale systems for machine learning.
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, multimodal, and dynamic possible worlds in social and 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) large-scale systems for machine learning.
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