John D. Lafferty
|John D. Lafferty|
|Alma mater||Princeton University|
|Known for||Conditional Random Fields|
IEEE Fellow (2007)|
Test-of-Time Award of ICML (2011,2012)
Classic paper prizes of ICML (2013)
Test of Time Award of SIGIR (2014)
University of Chicago
Carnegie Mellon University
Cheng Xiang Zhai
|Other notable students||David Blei (Post Dr.)|
John D. Lafferty is an American scientist, Professor at Yale University and leading researcher in machine learning. He is best known for proposing the Conditional Random Fields with Andrew McCallum and Fernando C.N. Pereira.
Lafferty is currently the John C. Malone Professor of Statistics and Data Science at Yale University, and has held positions at the University of Chicago, University of California, Berkeley and the University of California, San Diego. His research interests are in statistical machine learning, information retrieval, and natural language processing; focus on computational and statistical aspects of nonparametric methods, high-dimensional data and graphical models.
Prior to University of Chicago in 2011, he was faculty at Carnegie Mellon University since 1994， where he helped to found the world's first machine-learning department. Before CMU, he was a Research Staff Member at IBM Thomas J. Watson Research Center, where he worked on natural speech and text processing in the group led by Frederick Jelinek. Lafferty received a Ph.D. in Mathematics from Princeton University, where he was a member of the Program in Applied and Computational Mathematics. He was an assistant professor in the Mathematics Department at Harvard University before joining IBM.
Lafferty served many prestigious positions, including: 1) program co-chair and general co-chair of the Neural Information Processing Systems (NIPS) Foundation conferences; 2) co-director of CMU's new Ph.D. Machine Learning Ph.D. Program; 3) associate editor of the Journal of Machine Learning Research (JMLR)  and the Electronic Journal of Statistics; and 4) member of the Committee on Applied and Theoretical Statistics (CATS) of the National Research Council.
Lafferty received numerous awards, including two Test-of-Time awards at the International Conference on Machine Learning (ICML) 2011 & 2012, classic paper prize of ICML 2013, and Test-of-Time awards at the Special Interest Group on Information Retrieval (SIGIR) 2014.
- 1990. A statistical approach to machine translation.
- 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
- Test-of-Time Award of ICML 2011.
- 2002. Diffusion Kernels on Graphs and Other Discrete Input Spaces.
- Test-of-Time Award of ICML 2012.
- 2003. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.
- Classic paper prizes of ICML 2013.
- 2003. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval.
- Test of Time Award of SIGIR 2014.
- 2006. Dynamic topic models. ICML'06.
- "John Lafferty (IEEE Fellow in 2007)". IEEE. IEEE. 2007. Retrieved 15 December 2014.
- "Test-of-Time Award ICML'11". ICML. 2011. Retrieved 15 December 2014.
- "Test-of-Time Award ICML'12". ICML. 2012. Retrieved 15 December 2014.
- "Two classic paper prizes for papers that appeared at ICML 2013". ICML. 2013. Retrieved 15 December 2014.
- "SIGIR 2014 Best Paper Awards". SIGIR. 2014. Retrieved 15 December 2014.
- Peter F. Brown; John Cocke. "A statistical approach to machine translation". Computational Linguistics. MIT Press. 16 (2): 79–85. Retrieved 14 December 2014.
- "John Lafferty bio (ICMLA'06)" (PDF). ICMLA. 2006. Retrieved 15 December 2014.
- "JMLR Editorial Board". JMLR. Retrieved 15 December 2014.
- "Member Biographies (CATS)". Committee on Applied and Theoretical Statistics (CATS). National Research Council. Retrieved 15 December 2014.
- Philipp Koehn (2009). Statistical Machine Translation. Cambridge University Press. p. 17. ISBN 0521874157. Retrieved 22 March 2015.
In the late 1980s, the idea of statistical machine translation was born in the labs of IBM Research.