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
- Kamisetty Hetunandan, Postdoctoral Fellow, University of Washington
- Qirong Ho, Scientist, A*STAR, Singapore
Kim, Postdoctoral Fellow, Disney Lab
- Abhimanu Kumar, Senior Research Engineer, GageIn
- Judie Howrylak, Assistant Professor, Penn State University
- Seyoung Kim, Assistant Professor, Carnegie Mellon University
- Mladen Kolar, Assistant Professor, University of Chicago
Martins, Research Scientist, Priberam Labs and
Instituto Superior Técnico
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, Technical Lead, Voleon Inc.
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 next release is scheduled in April, 2015.
Video lectures of Probabilistic Graphical
(10708), made in Spring 2014.
I am teaching Probabilistic Graphical
(10708) again in Spring 2015.
In Fall 2014, I taught Advanced Machine
Learning (10715), a newly created required
course for ML Ph.D. students, with Prof.
Barnabas Poczos. The previous Graduate
Machine Learning (10701) is now a
general Ph.D.-level intro. ML for CMU students from all majors.
"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.