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:
Ahmed, Research Scientist, Google
- Maruan Al-Shedivat
- Ross Curtis, Software Engineer, AncestryDNA
- Bryon Aragam, Assistant Professor, University of Chicago
- Wei Dai, Research Engineer, Apple
- Kumar Avinava Dubey, Research Scientist, Google
- Jacob Eisenstein, Assistant
Professor, Georgia Institute of Technology
- Wenjie Fu, Software Engineer, Facebook
- Anuj Goyal, Software Engineer, LinkedIn
- Steve Hanneke, Visiting Assistant
Professor, Carnegie Mellon University
Hetunandan, Research Scientist, Facebook
- Qirong Ho, CTO, Petuum Inc.
- Judie Howrylak, Assistant Professor, Penn State University
- Zhiting Hu, Assistant Professor, UC San Diego
Kim, Assistant Professor, Seoul National University
- Jin Kyu Kim, Research Scientist, Facebook
- Abhimanu Kumar, Senior Research Engineer, LinkedIn
- Seyoung Kim, Assistant Professor, Carnegie Mellon University
- Mladen Kolar, Assistant Professor, University of Chicago
Lee, Research Scientist, Facebook
- Ben Lengerich, Postdoc, MIT
- Xiaodan Liang, Associate Professor, Zhongshan University
- Andre Martins, Research Scientist, Priberam Labs and
Instituto Superior Técnico
- Micol Marchetti-Bowick, Engineering Manager, Uber
- Willie Neiswanger, PostDoctoral Fellow, Stanford University
Parikh, Research Scientist, Google
Puniyani, Research Scientist, Google
- Pradipta Ray,
Research Scientist, University of Taxes Dallas
- Mrinmaya Sachan, Assistant Professor, ETH, Zurick
- Suyash Shringarpure, Postdoctoral researcher, Stanford University
- Kyung-Ah Sohn, Assistant Professor, Ajou University
Song, Associate Professor, Georgia Institute
Wang, Research Scientist, Google
- Jinliang Wei, Engineer, Google
Williamson, Assistant Professor, University of
Wilson, Assistant Professor, Cornell Unicersity
- Pengtao Xie, Assistant Professor, UC San Diego
Yin, Assistant Professor, University of
Yu, Assistant Professor, University of Waterloo
Zhao, VP of Machine Learning, Petuum Inc.
- Hao Zhang, PostDoc, UC Berkeley
- Xun Zheng, Research Scientist, Uber
- Bing Zhao, Research Scientist, SRI
- Jun Zhu, Professor, Tsinghua University
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. and CMU Sailing Lab on Distributed ML (e.g.,
Automated ML (e.g.,
and Composable ML (e.g.,
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):
Tutorials and Talks:
Thoughts and Efforts on AI Meeting Production
[video], Jeffrey L. Elman Distinguished Lecture Series, Halicioglu Data Science Inst., UC San Diego, 2021.
Simplifying and Automating Parallel Machine Learning via a Programmable and Composable Parallel ML System
Tutorial, AAAI 2021.
From Performance-oriented AI to Production- and Industrial-AI
Michigan Institute for Data Science, 2020.
A Blueprint of Standardized and Composable Machine Learning
Institute for Advanced Study, Princeton, 2020.
Learning from All Types of Experiences:
A Unifying Machine Learning Perspective
Tutorial, KDD 2020.
Compositionality in Machine Learning
Open Data Science Conference (ODSC) West 2019.
A Civil Engineering Perspective on Artificial Intelligence From Petuum
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
tutorial at the International Summer School on Deep Learning, Genova, Italy, 2018.
Strategies & Principles for Distributed Machine Learning
Allen Institute for AI, 2016.
Board Member, The International Machine Learning Society
Program Committee Chair, ICML
General Chair, ICML 2019.
Action Editor/Associate Editor: JASA, AOAS,
JMLR, MLJ, and PAMI.