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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

Picture of Wean Hall

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


Mohamed bin Zayed University of Artificial Intelligence

Founder, Petuum

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 Ph.D. Students and Postdocs:

Past Students and Postdocs:

Recent Activities:

Research and Development:

  • On June 11th, 2020, we launched the CASL (Composible, Automatic, and Scable ML) open source consortium that brings our research and development at Petuum Inc., CMU Sailing Lab, and collaborating labs on Distributed ML (e.g., AutoDist, AdaptDL, Alpa), Automated ML (e.g., Dragonfly, ProBO), and Composable ML (e.g., Texar, Forte) implemented across PyTorch and TensorFlow under a unified umbrella for a Production and Industrial AI Platform.


  • I taught Graduate Introduction to Machine Learning (10701) again in Fall 2020, with Professor Ziv Bar-Joseph

  • I have been teaching Probabilistic Graphical Models (10708), an advanced graduate course on theory, algorithm, and application for multivariate modeling, inference, and deep learning since 2005 at CMU. All the past versions are available here.
  • Video lectures of Probabilistic Graphical Models (10708): 2014, 2019, 2020.

    Tutorials and Talks:

  • From Learning, to Meta-Learning, to "Lego-Learning -- A pathway toward autonomous AI [video][slides], CMU AI Seminar, 2022.

  • It is time for deep learning to understand its expense bills [video], KDD Deep Learning Day 2021.

  • Learning-to-learn through Model-based Optimization: HPO, NAS, and Distributed Systems [video], ACL 2021 workshop on Meta Learning and Its Applications to Natural Language Processing.

  • A Data-Centric View for Composable Natural Language Processing [video1] [video2], ICML 2021 Machine Learning for Data Workshop.

  • Simplifying and Automating Parallel Machine Learning via a Programmable and Composable Parallel ML System [slides] [video], Tutorial, AAAI 2021.

  • From Performance-oriented AI to Production- and Industrial-AI [video], Michigan Institute for Data Science, 2020.

  • A Blueprint of Standardized and Composable Machine Learning [slides] [video], Institute for Advanced Study, Princeton, 2020.

  • Learning from All Types of Experiences: A Unifying Machine Learning Perspective [slides] [video], Tutorial, KDD 2020.

  • Compositionality in Machine Learning [slides] [video], Open Data Science Conference (ODSC) West 2019.

  • A Civil Engineering Perspective on Artificial Intelligence From Petuum [slides], Distinguished Lectures in Computational Innovation, Columbia University, 2018.

  • PetuumMed: algorithms and system for EHR-based medical decision support [slides], MIT, 2018.

  • A Statistical Machine Learning Perspective of Deep Learning: Algorithm, Theory, and Scalable Computing [slides], tutorial at the International Summer School on Deep Learning, Genova, Italy, 2018.

  • Strategies & Principles for Distributed Machine Learning [slides], [video], Allen Institute for AI, 2016.


  • 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 7/4/2022