John Paisley


422 S.W. Mudd

Tel(212) 854-8024

John Paisley’s research focuses on developing models for large-scale text and image processing applications. He is particularly interested in Bayesian models and posterior inference techniques that address the big data problem.

Research Interests

General area of statistical machine learning, probabilistic modeling and inference techniques, Bayesian nonparametic methods, dictionary learning, topic modeling

Paisley joined the Department of Electrical Engineering at Columbia University in Fall 2013 and is an affiliated faculty member of the Data Science Institute at Columbia University. He received the BS, MS and PhD degrees in electrical and computer engineering from Duke University in 2004, 2007 and 2010. He was then a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley. 


  • Postdoctoral fellow, EECS Computer Science Division, University of California, Berkeley, 2011-2013
  • Postdoctoral fellow, Department of Computer Science, Princeton University, 2010-2011


  • Assistant professor of electrical engineering, Columbia University, 2013 – 


  • IEEE
  • ACM
  • ISBA
  • Senior Program Committee, International Conference on Machine Learning, 2015
  • Senior Program Committee, Artificial Intelligence and Statistics, 2014, 2015, 2017
  • Senior Program Committee, International Joint Conference on Artificial Intelligence, 2015, 2016, 2017
  • Frequent reviewer for IEEE Transactions on Image Processing, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Signal Processing, Journal of Machine Learning Research


  • Distinguished Faculty Teaching Award, Columbia Engineering Alumni Association, 2017
  • Top 10% Paper Recognition, IEEE International Conference on Image Processing, 2013
  • Notable Paper Award, International Conference on Artificial Intelligence and Statistics, 2011
  • Confucius Institute Language Scholarship, Xiamen University (host), 12/2011 – 1/2012
  • Charles R. Vail Outstanding Graduate Scholarship Award, Duke University, May 2010
  • David Randall Fuller Prize, Duke University, May 2004


  • X. Fu, J. Huang, X. Ding, Y. Liao and J. Paisley (2017). Clearing the skies: A deep network architecture for single-image rain removal, IEEE Transactions on Image Processing (to appear).
  • V. Chen, J. Paisley and X. Lu (2017). Revealing common disease mechanisms shared by tumors of different tissues of origin through semantic representation of genomic alterations and topic modeling, BMC Genomics, 8(Suppl 2):105.
  • X. Fu, D. Zeng, Y. Huang, Y. Liao, X. Ding and J. Paisley (2016). A fusion-based enhancing method for weakly illuminated images, Signal Processing, vol. 129, pp. 82-96.
  • J. Paisley, C. Wang, D. Blei and M.I. Jordan (2015). Nested hierarchical Dirichlet processes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, no. 2, pp. 256-270.
  • T. Broderick, L. Mackey, J. Paisley and M.I. Jordan (2015). Combinatorial clustering and the beta negative binomial process, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 37, no. 2, pp. 290-306.
  • Y. Huang, J. Paisley, Q. Lin, X. Ding, X. Fu and X. Zhang (2014). Bayesian nonparametric dictionary learning for compressed sensing MRI, IEEE Trans. on Image Processing, vol. 23, no. 12, pp. 5007-5019.
  • M. Hoffman, D. Blei, C. Wang and J. Paisley (2013). Stochastic variational inference, Journal of Machine Learning Research, vol. 14, pp. 1303-1347.
  • J. Paisley, C. Wang and D. Blei (2012). The discrete infinite logistic normal distribution, Bayesian Analysis, vol. 7, no. 2, pp. 235-272.
  • M. Zhou, H. Chen, J. Paisley, L. Ren, L. Li, Z. Xing, D. Dunson, G. Sapiro and L. Carin (2012). Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images, IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 130-144.
  • J. Paisley, X. Liao and L. Carin (2010). Active learning and basis selection for kernel-based linear models: a Bayesian perspective, IEEE Transactions on Signal Processing, vol. 58, no. 5, pp. 2686-2700.