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Eric P. Xing,  PhD, PhD

8101 Gates-Hillman Center (GHC), SCS
Carnegie Mellon University
Pittsburgh, PA 15213

Phone: (412) 268-2559
Fax: (412) 268-3431
Email: epxing AT

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Biography Publications Research Teaching Activities/Talks My Group CV


Machine Learning Department
Language Technology Institute & Computer Science Department
School of Computer Science
Carnegie Mellon University

Co-Founder, Petuum

CSRankings adjusted score and ranking: 45.6, #1

Research synopsis: My principal research interests lie in the development of machine learning and statistical methodology, and large-scale computational system and architecture, for solving problems involving automated learning, reasoning, and decision-making in high-dimensional, multimodal, and dynamic possible worlds in artificial, biological, and social systems.

Current Students and Postdocs:

Past Students and Postdocs:

Recent Activities:

In 2016, I founded Petuum Inc. to pursue standardization and industrialization of general-purpose AI platform and building blocks. I have been serving as CEO and Chief Scientist of Petuum, which was recognized as a Technology Pioneer by the World Economic Forum in 2018.

Research and Development:

  • On December 25th, 2013, we made an initial open-source release of Petuum, a new framework for distributed machine learning with massive data, big models, and a wide spectrum of algorithms. Updates on Petuum are released every three months. The latest release (version 1.1) was made in July, 2015.


  • I am teaching Probabilistic Graphical Models (10708) again in Spring 2019! Half of the contents will be NEW!

  • Video lectures of Probabilistic Graphical Models (10708), made in Spring 2014.
  • In Fall 2014, I taught Advanced Machine Learning (10715), a newly created required course for ML Ph.D. students, with Prof. Barnabas Poczos.
  • I regularly teach Graduate Machine Learning (10701), which is a general Ph.D.-level intro. ML for CMU students from all majors.


  • "A New Look at the System, Algorithm and Theory Foundations of Distributed Machine LearningA Statistical Machine Learning Perspective of Deep Learning: Algorithm, Theory, and Scalable Computing" [slides], at the International Summer School on Deep Learning, Genova, Italy, 2018.

    "System and Algorithm Co-Design, Theory and Practice, for Distributed Machine Learning" [video], at the Simons Institute for the Theory of Computing, Berkeley, 2017.

  • "A New Look at the System, Algorithm and Theory Foundations of Distributed Machine Learning" [slides], with Dr. Qirong Ho at the 21st ACM SIGKDD Conference on knowledge Discovery and Data Mining (KDD 2015).

  • "Big ML Software for Modern ML Algorithms" [slides], with Dr. Qirong Ho at the 2014 IEEE International Conference on Big Data (IEEE BigData 2014).

  • "Topic Models, Latent Space Models, Sparse Coding, and All That: A systematic understanding of probabilistic semantic extraction in large corpus" [slides], at the 50th Annual Meeting of the Association for Computational Linguistics (ACL 2012).

  • "Modern Statistical Methods for Genetic Association Study: Structured Genome-Transcriptome-Phenome Association Analysis" [slides], With Dr. Seyoung Kim, at the Nineteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2011).


  • Board Member, The International Machine Learning Society .
  • Program Committee Chair, ICML 2014.
  • General Chair, ICML 2019.
  • Action Editor/Associate Editor: JASA, AOAS, JMLR, MLJ, and PAMI.

  • Last updated 01/08/2019