Eric P. Xing
homepage:http://www.cs.cmu.edu/~epxing/
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Description

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.


Lectures:

invited talk
flag SysML: On System and Algorithm co-design and Automatic Machine Learning
as author at  24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), London 2018,
939 views
  lecture
flag Joint Max Margin and Max Entropy Learning of Graphical Models
as author at  Large Scale Graphical Models,
5267 views
lecture
flag Time Varying Graphical Models: Reverse Engineering and Analyzing Rewiring Networks
as author at  Mini Symposia,
4878 views
  lecture
flag Recent Advances in Learning Sparse Structured Input/Output Model: Models, Algorithms, and Applications
as author at  NIPS Workshop on Structured Input - Structured Output, Whistler 2008,
4317 views
lecture
flag Some Challenging Machine Learning Problems in Computational Biology: Time-Varying Networks Inference and Sparse Structured Input-Out Learning
as author at  Carnegie Mellon Machine Learning Lunch seminar,
7914 views
  lecture
flag A Joint Topic and Perspective Model for Ideological Discourse
as author at  European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Antwerp 2008,
together with: Wei-Hao Lin, Alexander Hauptmann,
3390 views
lecture
flag Dynamic Non-Parametric Mixture Models and The Recurrent Chinese Restaurant Process
as author at  Workshops,
5284 views
  lecture
flag Generative Latent Space Models for Text and Image
as author at  Autumn School 2006: Machine Learning over Text and Images - Pittsburgh,
9279 views
interview
flag Interview with Eric Xing
as interviewee at  Autumn School 2006: Machine Learning over Text and Images - Pittsburgh,
9351 views
  introduction
flag Introduction to the Machine Learning over Text & Images - Autumn School by Eric Xing
as author at  Autumn School 2006: Machine Learning over Text and Images - Pittsburgh,
14515 views
lecture
flag Undirected Graphical Models for Text & Image
as author at  Autumn School 2006: Machine Learning over Text and Images - Pittsburgh,
6808 views