John Paisley

Electrical Engineering

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.

  • 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.