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
- Ross Curtis, Software Engineer, AncestryDNA
- 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, Scientist, A*STAR, Singapore
- Judie Howrylak, Assistant Professor, Penn State University
Kim, Assistant Professor, Seoul National University
- Abhimanu Kumar, Senior Research Engineer, LinkedIn
- Seyoung Kim, Assistant Professor, Carnegie Mellon University
- Mladen Kolar, Assistant Professor, University of Chicago
LeeResearch Scientist, Human Longevity Inc.
Martins, Research Scientist, Priberam Labs and
Instituto Superior Técnico
Parikh Research Scientist, Google
Puniyani, Research Scientist, Google
- Pradipta Ray,
Research Scientist, University of Taxes Dallas
- Suyash Shringarpure, Postdoctoral researcher, Stanford University
- Kyung-Ah Sohn, Assistant Professor, Ajou University
Song, Assistant Professor, Georgia Institute
Wang, Research Scientist, Beidu
Williamson, Assistant Professor, University of
Yin, Assistant Professor, University of
- Bing Zhao, Research Scientist, SRI
- Jun Zhu, Associate Professor, Tsinghua University
Research and Development:
On December 25th, 2013, we made an initial
open-source release of Petuum,
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.
Video lectures of Probabilistic Graphical
(10708), made in Spring 2014.
I am teaching Graduate
(10701) again in Fall 2015, with Prof.
In Fall 2014, I taught Advanced Machine
Learning (10715), a newly created required
course for ML Ph.D. students, with Prof.
Poczos. Previously I taught Graduate
Machine Learning (10701), which is now 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 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
Program Committee Chair, ICML 2014.
Action Editor/Associate Editor: JASA, AOAS,
JMLR, MLJ, and PAMI.