Eric Xing's home page (original) (raw)

| | Biography | Publications | Research | Teaching | Activities/Talks | My Group | CV | | | -------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------------ | -------------------------- | |

Professor

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

President

Mohamed bin Zayed University of Artificial Intelligence

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.

Current Ph.D. Students and Postdocs:

Past Students and Postdocs:

Amr Ahmed, Research Scientist, Google
Maruan Al-Shedivat, Principal Research Scientist, Genesis Therapeutics
Bryon Aragam, Assistant Professor, University of Chicago
Sangkeun Choe, Anthropics
Ross Curtis, Software Engineer, AncestryDNA
Wei Dai, Research Engineer, Apple
Kumar Avinava Dubey, Research Scientist, Google
Jacob Eisenstein, Assistant Professor, Georgia Institute of Technology
Wenjie Fu, Software Engineer, Facebook
Xuchen Gong, PhD Student, UChicago
Anuj Goyal, Software Engineer, LinkedIn
Steve Hanneke, Visiting Assistant Professor, Carnegie Mellon University
Kamisetty Hetunandan, Research Scientist, Facebook
Qirong Ho, CTO, Petuum Inc.
Judie Howrylak, Assistant Professor, Penn State University
Zhiting Hu, Assistant Professor, UC San Diego
Gunhee Kim, Associate Professor, Seoul National University
Jin Kyu Kim, Research Scientist, Facebook
Abhimanu Kumar, Senior Research Engineer, LinkedIn
Seyoung Kim, Associate Professor, Carnegie Mellon University
Mladen Kolar, Assistant Professor, University of Chicago
Lisa Lee, Research Scientist, Google
Seunghak Lee, Research Scientist, Facebook
Ben Lengerich, Postdoc, MIT
Xiaodan Liang, Associate Professor, Zhongshan University
Andre Martins, Research Scientist, Priberam Labs and Instituto Superior Tecnico
Micol Marchetti-Bowick, Engineering Manager, Uber
Willie Neiswanger, Assistant Professor, University of Southen California
Ankur Parikh, Staff research scientist at Google, adjunct assistant professor at NYU.
Kriti Puniyani, Research Scientist, Google
Aurick Qiao, Snowflake
Pradipta Ray, Research Scientist, University of Taxes Dallas
Mrinmaya Sachan, Assistant Professor, ETH, Zurick
Suyash Shringarpure, Senior Scientist, Statistical Genetics at 23andMe
Kyung-Ah Sohn, Assistant Professor, Ajou University
Le Song, Professor, MBZUAI
Chong Wang, Research Scientist, Google
Jinliang Wei, Engineer, Google
Sinead Williamson, Assistant Professor, University of Texas Austin
Andrew Wilson, Professor, NYU
Haohan Wang, Assistant Professor, UIUC
Hongyi Wang, Director of Infrastructure, GenBio AI; Assistant Professor, Rutgers
Pengtao Xie, Assistant Professor, UC San Diego
Junming Yin, Assistant Professor, University of Arizona
Yaoliang Yu, Assistant Professor, University of Waterloo
Bin Zhao, Entrepreneur
Hao Zhang, Assistant Professor, UC San Diego
Xun Zheng, Research Scientist, Uber
Bing Zhao, Research Scientist, SRI
Jun Zhu, Professor, Tsinghua University

Recent Activities: Research and Development:

Lately (2024), we have launched:

AIDO (AI-Driven Digital Organism), a system of multiscale foundation models for predicting, simulating, and programming biology at all levels.
PAN (Pandora), a step towards an "AllWorld" (ToutMonde) model that simulates a controlable and immersive virtualization of the Physical, Agent, and Networked universe - a PAN universe - where all human reasoning and actions can carry out virtually.
LLM360, an open research lab enabling community-owned AGI through 360-open-source LLM research and development. 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. 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. 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.