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Paul D. Gader, Ph.D.


Research Areas: 
Database, Data Science and Informatics
Image and Signal Analysis
Machine Learning
  • Ph.D., University of Florida, 1986
  • M.S., University of Florida, 1983
  • B.S., University of Central Florida 1981
Research Interests: 

Paul Gader received his Ph.D. in Mathematics for parallel image processing and applied mathematics research in 1986 from the University of Florida. He has worked as a Senior Research Scientist at Honeywell’s Systems and Research Center, as a Research Engineer and Manager at the Environmental Research Institute of Michigan (ERIM), and as a faculty member at the University of Wisconsin - Oshkosh, and the University of Missouri - Columbia. He joined the faculty of the University of Florida in August 2001. He performed his first research in image processing in 1984 when he worked on algorithms for detection of bridges in Forward Looking Infra-Red (FLIR) imagery. At ERIM and later at the University of Missouri, he led teams involved in the research and development of real-time, handwritten address recognition systems for the U.S. Postal Service. He developed, implemented, and tested image processing, neural network, and fuzzy set based algorithms for handwritten digit recognition and segmentation, numeric field recognition, word recognition and segmentation, and line segmentation. He has also worked on a number of other image and signal analysis projects, including medical imaging, vehicle detection and recognition, acoustic signature analysis, and bio-medical pattern recognition as well as performing fundamental research in image algebra, fuzzy set theory, and and Choquet integral based mathematical morphology. He has actively researched and developed algorithms for land mine research since 1996. He has led teams that devised and field tested several real-time algorithms for mine detection. He served as Technical Director of the University of Missouri MURI on Humanitarian Demining. Past and present landmine detection projects involve algorithm development for data generated from hand-held, ground vehicle-based, and airborne sensors, including ground penetrating radar, acoustic/seismic, IR (emissive and reflective bands), long-wave, short-wave, near IR, and visible hyperspectral imagery, and wide-band electro-magnetic sensors. He has investigated a wide variety of algorithmic approaches in the context of solving real-world problems. In the last few years, his interests have expanded into the domains of hyperspectral image analysis. Dr. Gader is a senior member of the IEEE and has over 245 technical publications in the areas of image and signal processing, applied mathematics, and pattern recognition, including over 74 refereed journal articles.

Current and Recent Courses: 
Fall 2010CAP 6617Advanced Machine Learning
Spring 2010CIS 6930Elements Stat Learning
Fall 2008CAP 6930Hidden Markov Models
Fall 2007CAP 6615Neural Networks
Spring 2007CIS 6930Fuzzy Logic


  • J. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. D. Gader, J. Chanussot, “Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5, No. 2, pp: 354 – 379, April, 2012.
  • A. Zare, P. D. Gader, G. Casella, “Sampling Piece-wise Convex Unmixing and Endmember Extraction”, IEEE Trans. Geoscience and Remote Sensing, vol.51, no. 3, 2013 , pp. 1655-1665, March, 2013.
  • H. Frigui and P. D. Gader, “Detection and discrimination of land mines in ground-penetrating radar based on edge histogram descriptors and a Possibilistic K-Nearest Neighbor Classifier”, IEEE Trans. Fuzzy Systems, Volume 17, Issue 9, March 2009, Page(s) 185-199.
  • K. C. Ho and P. D. Gader, “A Linear Prediction Land Mine Detection Algorithm for Hand Held Ground Penetrating Radar”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 40, No. 6, pp. 1374-1385, June, 2002.
  • S. Yuksel, J. Wilson, and P. D. Gader, "Twenty Years of Mixture of Experts”, IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 8, p.1177-1193, May, 2012.
  • J. Bolton, P. D. Gader, “Random Set Framework for Context-Based Classification with Hyperspectral Imagery”, IEEE Trans. Geoscience and Remote Sensing, Vol. 47, No. 11, Nov. 2009, Page(s): 3810-3821.
  • P. D. Gader, M. Khabou, and A. Koldobsky, “Morphological Regularization Neural Networks,” Pattern Recognition, Special Issue on Mathematical Morphology and Its Application, Vol. 33, No. 6, pp. 935-945, June 2000.
  • N. Theera-Umpon and P. D. Gader, “Counting White Blood Cells Using Morphological Granulometries, Journal of Electronic Imaging, Vol. 9, No. 2, pp. 170-177, April 2000.
  • W. Chen, P. D. Gader, H. Shi, “Lexicon Driven Handwritten Word Recognition Using Optimal Linear Combinations of Order Statistics,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21, No. 1, pp.77-83, Jan. 1999.
  • P. D. Gader, J. M. Keller, R. Krishnapuram, J.H. Chiang, and M. Mohamed, \\“Neural and Fuzzy Methods in Handwriting Recognition,\\”IEEE Computer, Vol. 30, No. 2, pp. 79-86, Feb. 1997.
  • Fellow, IEEE
  • UF Research Foundation Professor