John Fisher

John Fisher

Biography

John Fisher is Principal Research Scientist at the MIT Computer Science and Artificial Intelligence Laboratory. His research focuses on information-theoretic approaches to machine learning, computer vision, and signal processing. Application areas include signal-level approaches to multi-modal data fusion, signal and image processing in sensor networks, distributed inference under resource constraints, resource management in sensor networks, and analysis of seismic and radar images. In collaboration with the Surgical Planning Lab at Brigham and Women's Hospital, he is developing nonparametric approaches to image registration and functional imaging.

He received a BS and MS in Electrical Engineering at the Univsersity of Florida in 1987 and 1989, respectively. He earned a PhD in Electrical and Computer Engineering in 1997.

Publications

  • Mujdat Cetin, Lei Chen, John Fisher, Alexander Ihler, Randolph Moses, and Alan Willsky. Distributed fusion in sensor networks: A graphical models perspective. IEEE Signal Processing Magazine, 2006.
  • Kilian Pohl, J. Fisher, W.E.L. Grimson, R. Kikinis, and W.M. Wells. A bayesian model for joint segmentation and registration. Neuroimage, 2006.
  • A. T. Ihler, J. W. Fisher III, and A. S. Willsky. Loopy belief propagation: Convergence and effects of message errors. Journal of Machine Learning Research, May 2005.
  • Junmo Kim, John W. Fisher III, Anthony Yezzi Jr., Mujdat Cetin, and Alan S. Willsky. Nonparametric methods for image segementation using information theory and curve evolution. IEEE Transactions on Image Processing, 2004.
  • John W. Fisher III and Trevor Darrell. Speaker association with signal-level audiovisual fusion. IEEE Transactions on Multimedia, Jun 2004.
  • A. T. Ihler, J. W. Fisher III, and A. S. Willsky. Nonparametric hypothesis tests for statistical dependency. IEEE Transactions on Signal Processing, August 2004.
  • Erik G. Learned-Miller and John W. Fisher III. Ica using spacings estimates of entropy. Journal of Machine Learning Research, special issue on Independent Components Analysis, Dec 2003.

Awards

  • Neural Information Processing Systems (NIPS): Best paper (2010)