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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 cs.cmu.edu

Picture of Wean Hall



Biography Publications Research Teaching Activities/Talks My Group CV
 

President

Mohamed bin Zayed University of Artificial Intelligence

Professor

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

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.

In recent years, I have been focusing on building large language models, world/agent models, and foundation models for biology.

Recent Activities:

Lately (2025), we have launched:

  • AIDO (AI-Driven Digital Organism), a system of multiscale foundation models for predicting, simulating, and programming biology at all levels.

  • PAN-World, a true world model builds an action-conditioned simulation for counterfactual reasoning, foresight, planning and safe action.

  • K2/K2-Think, leading open-source LLM/system that delivers frontier capabilities and advanced AI Reasoning.


    • Current Ph.D. Students and Postdocs:




    Past Activities:


    Past Students and Postdocs:



    Tutorials and Talks (Selected):

  • Toward General and Purposeful Reasoning in Real World Beyond Lingual Intelligence [video], Columbia Engineering's Lecture Series in AI, 2025.

  • Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating, and Programming Biology at All Levels [video], Princeton University, 2025.

  • An OPEN Path to Super Intelligence [video], PyTorch Conference, 2025.

  • Toward Next-Generation AI Systems Beyond Lingual Intelligence [video], AI Action Summit, 2025.

  • Is the Path to Empowerment Paved by AI? [podcast], The Deep View: Conversations, 2024.

  • Federated Learning, Model Parallelism, and Variational Inference [video], ICLR 2023 Workshop on Machine Learning for IoT: Datasets, Perception, and Understanding.

  • 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.



    • Teaching:

  • 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.



    • Services:

  • 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 12/28/2025