John D. Lafferty
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.Biography
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.
He was elected Fellow of IEEE in 2007 "for contributions to statistical pattern recognition and statistical language processing".Academic career
Lafferty served many prestigious positions, including: 1) program co-chair and general co-chair of the Neural Information Processing Systems co-director of CMU's new Ph.D. Machine Learning Ph.D. Program; 3) associate editor of the Journal of Machine Learning Research and the Electronic Journal of Statistics; and 4) member of the Committee on Applied and Theoretical Statistics of the National Research Council.
Lafferty received numerous awards, including two Test-of-Time awards at the International Conference on Machine Learning 2011 & 2012, classic paper prize of ICML 2013, and Test-of-Time awards at the Special Interest Group on Information Retrieval 2014.Selected works
- 1990. A statistical approach to machine translation.
- 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.
- 2002. Diffusion Kernels on Graphs and Other Discrete Input Spaces.
- 2003. Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions.
- 2003. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval.
- 2006. Dynamic topic models. ICML'06.
