Jason Kinser

Summary

Chair and Associate Professor, Computational and Data Sciences, College of Science, George Mason University

Information

Web:  CDS Page  LinkedIn

Email:  jkinser@gmu.edu
Phone:703-993-3785
Address:  230 Research Hall
4400 University Blvd.
Fairfax, VA 22030      

Education

D.Sc. in Optics and Electro-Optical Systems, South Eastern Inst. of Tech., Dec. 1993

M.S. in Physics, Univ. Al. in Huntsville, May 1987

B.A. in Physics, William Jewell College, May 1985

External Examiner Appointment in University of Mauritius, June 2003-05, Computational Sciences.

Experience

GMU
Assoc. Professor
Apr 1997 – Present
Alabama A&M University
Assoc. Research Professor
Alabama A&M University
1993 – 1997
University of Alabama in Huntsville
CAO
University of Alabama in Huntsville
1985 – 1991 Employment

Current Research Interests

Image analysis and multi-domain data mining.

Publications

Books

1. J. Kinser, Python for Bioinformatics, Jones and Bartlett, March 2009

2. T. Lindblad & J. Kinser, Image Processing using Pulsed Coupled Neural Networks, Springer-Verlag, London, (first edition) 1998, (second edition) 2005, (Chinese edition 2008), (third edition) 2013.

3. J. Kinser, Kinematic Labs with Mobile Devices (IOP Concise Physics), 2016.

4. J. Kinser, Computational Methods for Bioinformatics, 2017.

5. J. Kinser, (working title) Image Operators, 2018

Book Chapters

1. J. M. Kinser, Pulse Image Processing, Soft Computing and Industry, Heather Luke, Rajkumar Roy, Mario Koeppen, Seppo Ovaska eds., Springer-Verlag, 411-422 (2002).

2. J. M. Kinser, Pulse Images for Face Recognition, in Face Recognition from Theory to Applications, H. Wechsler, et. al., eds., Springer Verlag 1998

3. M. A. G. Abushagur, H. J. Caulfield, J. Kinser, R. J. Berinato, G. L. Henderson, Optical Signals and Image Processing, in Perspectives in Optoelectronics, S. S. Jha ed., World Scientific, (1995).

4. H-I. Jeon, J. Shamir, R. B. Johnson, H. J. Caulfield, J. M. Kinser, C. Hester, M. Temmen, “The Use of Fixed Holograms for Massively-Interconnected, Low-Power, Neural Networks”, in Neural Networks for Perception, H. Wechsler ed., Academic Press, (1992).

Other Publications

  1. G. Wang, J. Kinser, “Nonlinear Dimensionality Reduction on 3-D Protein Image Analysis”, ASIMOLAR, 2007.
  2. J. Kinser, “Geodesically Reduced Image Primitive Space”, IASTED Signal and Image Processing, in press.
  3. G. Wang, J. Kinser, “Towards Content Based Object Recognition with Image Primitive”, Proceedings of the SPIE, 6064, 1H-1 – 1H-8, (2006).
  4. A. Chikando, J. Kinser, “Adaptation of fast marching methods to subcellular modeling.” Proceedings of the SPIE, 6065-27, (2006).
  5. J. M. Kinser, “Image Primitives”, presented at WSEAS 05.
  6. J. M. Kinser, “Image Primitives for Content Based Image Retrieval”, WSEAS Transactions on Signal Processing, 3(1), 377-384, (2005).
  7. J. Kinser, G. Wang, “Content Based Object Retrieval with Image Primitive Database”, AIPR 05, R. J. Bonneau, ed., 179-183, (2005)
  8. A. Chikando, J. Kinser, “Optimizing Image Segmentation Using Color Model Mixtures”, AIPR 05, R. J. Bonneau, ed., 230-235, (2005)
  9. G. Wang, J. Kinser, “Texture Discrimination and Classification Using Pulse Images”, Applied Image and Pattern Recognition, AIPR04, Washington DC, 2004.
  10. J. Kinser, “Image Primitive Signatures”, Applied Image and Pattern Recognition, AIPR04, Washington DC, 2004.
  11. U. Ekblad, J. M. Kinser, “Theoretical Foundation of the Intersecting Cortical Model and its Use for Change Detection of Aircraft, Cars and Nuclear Explosion Tests”, Signal Processing, 84(7), 1131-1146 (2004).
  12. J. M. Kinser, “Accelerated Queries of Time Series Database”, Pattern Recognition, 37(8), 1691-1698, (2004).
  13. J. M. Kinser, G. Wang, “Texture recognition of medical images with the ICM method”, Nuclear Inst. and Methods in Physics Research, A, Elsevier, 525(1-2), 387-391, (2004).
  14. U. Ekblad, J. M. Kinser, J. Atmer, N. Zetterlund, “The Intersecting Cortical Model in Image Processing”, accepted by Nuclear Inst. and Methods in Physics Research, A, Elsevier, 525(1-2), 392-396, (2004).
  15. U. Ekblad, J. M. Kinser, J. Atmer, N. Zetterlund, “Image Information Content and Extraction Techniques”, Nuclear Inst. and Methods in Physics Research, A, Elsevier, 525(1-2), 397-401, (2004).
  16. J. M. Kinser, “Biological Models for Image Processing”, Encyclopedia of Optical Engineering, 151-165, 2003.
  17. J. M. Kinser, G. Wang, “Texture Recognition in Medical Images with ICM Method”, Imaging 2003 Intl. Conf. On Imaging Techniques in Subatomic Physics, Astrophysics, Medicine, Biology and Industry, June 2003.
  18. U. Ekblad, J.M. Kinser, N. Zetterlund, “The Unified Cortical Model in Image Processing”, Imaging 2003 Intl. Conf. On Imaging Techniques in Subatomic Physics, Astrophysics, Medicine, Biology and Industry, June 2003.
  19. U. Ekblad, J.M. Kinser, J. Atmer, “Image Information Content and Extraction Techniques”, Imaging 2003 Intl. Conf. On Imaging Techniques in Subatomic Physics, Astrophysics, Medicine, Biology and Industry, June 2003.
  20. J. M. Kinser, “High Speed Content Based Image Retrieval”, Imaging 2003 Intl. Conf. On Imaging Techniques in Subatomic Physics, Astrophysics, Medicine, Biology and Industry, June 2003.
  21. J. M. Kinser, “Pulse Image Processing”, in Recent Developments in Pattern Recognition, S. G. Pandalai, ed. 3, 321-352, (2002).
  22. M. Kafatos, H. El-Askary, L. Chiu, R. Gomez, M. Hegazy, J. Kinser, X. Liu, Y. Liu, Z. Liu, J. McManus, Y. Nie, J. Qu,, F. Salem, S. Sarkar, S. Shen, G. Taylor, H. Wolf, D. Wong, C. Yang, R. Yang, “Remote sensing and GIS for regional environmental applications”
  23. Proceedings of SPIE – The International Society for Optical Engineering 4886, pp. 1-10, (2002).
  24. “Development of a Web Image Browser Through Image Signatures” , J. M. Kinser, Proc. of the 5th IASTED Intl. Conf. on Software Engineering and Applications, 2001, 32-36.
  25. “Image Signatures: Classification and Ontology”, J. M. Kinser, Proc. of the 4th IASTED Intl. Conf. on Computer Graphics and Imaging, 2001.
  26. “Pulse image processing using centripetal autowaves”, J. M. Kinser, C. Nguyen, Proc. SPIE, 4052, April 2000, 278-284.
  27. “Parameter estimation using dual fractional power filters”, J. M. Kinser, Proc. SPIE, April 2000, 4044, 29-36.
  28. “Minimum number of hidden neurons does not necessarily provide the best generalization”, J. M. Kinser, Proc. SPIE, 4055, 11-17,
  29. “Composite fractional power wavelets”, J. M. Kinser, Proc. SPIE, 4056, 443-441, April 2000.
  30. “Image object signatures from centripetal autowaves”, J. M. Kinser, C. Nguyen, Pattern Recognition Letters, 21(3), 221-225, (2000).
  31. “Mining DNA Data in an Efficient 2D Optical Architecture”, J. M. Kinser, Proc. SPIE, Optical Computing, Quebec, 4089, 104-110, (2000).
  32. “Automatic inspection of road surfaces”, Rughooputh, Harry C.; Rughooputh, Soonil D.;Kinser, Jason M., Proc. SPIE, 3966, 349-357, (2000).
  33. “Neural network based automated texture classification system”, Rughooputh, Harry C.; Rughooputh, Soonil D.; Kinser, Jason M., Proc. SPIE, 3966, 340-349, (2000).
  34. “Multidimensional Pulse Image Processing of Chemical Structure DataÓ, J. M. Kinser, K. Waldemark, T. Lindblad and S.P. Jacobsson, Chemom. Intell. Lab. Syst., 51 (2000) 115-124.
  35. “Foveation from Pulse Images”, J. M. Kinser, Proc. Of IEEE Conf. On Information Intelligence and Systems, 86-89, (1999).
  36. “Inherent Features of Wavelets and Pulse Coupled Neural Networks, Th. Lindblad, J. M. Kinser, IEEE Trans. on Neural Nets 10(3), 607-615 (1999).
  37. “Implementation of the Pulse-Coupled Neural Network in a CNAPS Environment”, J. M. Kinser, Th. Lindblad, IEEE Trans. on Neural Nets 10(3), 591-599 (1999).
  38. “Foveation by a Pulse-Coupled Neural Network”, J. M. Kinser, IEEE Trans. on Neural Nets 10 (3), 621-626 (1999).
  39. “Finding the shortest path in the shortest time using PCNNs”, H. J. Caulfield, J. M. Kinser, IEEE Trans. on Neural Nets 10(3), 604-607 (1999).
  40. “A neural bridge from syntactic to statistical pattern recognition”, Frank T. Allen, Jason M. Kinser, H. John Caulfield, Neural Networks 12(3) (1999) pp.519-526.
  41. “Spiral Fusion Using Interchannel Autowaves”, J. M. Kinser, Proc. Of SPIE, 3728, Stockholm, June 1998,148-154.
  42. (keynote) “Future Projects In Pulse Image Processing”, J. M. Kinser, Proc. Of SPIE, 3728, Stockholm, June 1998, 318-327
  43. J. M. Kinser, “Hardware: Basic Requirements for Implementation”, Proc. Of SPIE, 3728, Stockholm, June 1998, 222-229.
  44. “Pulse-Coupled Neural Networks for Cruise Missile Guidance and Mission Planning”, Proc. Of SPIE, 3728, Stockholm, June 1998, 155-164.
  45. “Pulse-Coupled Neural Networks for Cruise Missile Guidance & Mission Planning”, J. T. Waldemark, V. Becanovic, Th. Lindblad, C. S. Lindesy, K. E. Waldemark, J. M. Kinser, Proc. Of SPIE, 3728, Stockholm, June 1998, 155-164.
  46. “Comparison of Artificial and Natural Neural Computations of an Application to Automatic Target Recognition”, K. E. Waldemark, V. Becanovic, J. M. Kinser, Th. Lindblad, C. S. Lindesy, G. Szekely, Proc. Of SPIE, 3728, Stockholm, June 1998, 165-181.
  47. “A Foveating – Fuzzy Scoring Target Recognition System”, R. Srinivasan, J. M. Kinser, Pattern Recognition 31(8) 1149-1158 (1998).
  48. “Spiral image fusion: a 30 parallel channel case”, Kinser, Jason M.; Wyman, Charles L.; Kerstiens, Bernard L., Optical Engineering 37(02), 492-498, 1998
  49. “Detection of microcalcification by cortical simulation”, J. Kinser and Th. Lindblad, EANN’97 Stockholm, June, 1997, Neural Networks in Engineering Systems, ISBN 952-90-8667-9, p. 203-206, A. B. Bulsari and S. Kalli, eds.
  50. “Pulse-Coupled Image Fusion”, J. M. Kinser, Optical Eng., 36 (3), 737-742 (1997).
  51. “Pulse Images for Face Recognition”, J. M. Kinser, Face Recognition: From Theory to Applications, H. Wechsler, P J. Phillips, V. Bruce, F. Fogelman SouliŽ, T. S. Huang, eds., NATO ASI Series, 163, 503-512, (1997).
  52. “Bioneuralogical Systems”, J. M. Kinser, H. J. Caulfield, in Virtual Intelligence, SPIE 2878, 60-69 (1996)
  53. “Synergistic Pulse-Coupled Neural Network Pattern Recognition”, J. M. Kinser, J. L. Johnson, H. J. Caulfield, Optical Memories and Neural Networks, 5(3), 179-183 (1996).
  54. “Object Isolation”, J. M. Kinser, Optical Memories and Neural Networks, 5>(3), 137-145 (1996).
  55. “Object Isolation Using a Pulse-Coupled Neural Network”, Proc. of the SPIE, 2824, 70-77, (1996).
  56. “Syntactical Computing Using Pulse-Coupled Neural Network Modules”, J. M. Kinser, Proc. of the SPIE, 2824, 77-83, (1996).
  57. “Hearing Shapes: Auditory Recognition of Two-Dimensional Spatial Patterns”, P. Simmons, H. J. Caulfield, J. L. Johnson, M. P. Schamschula, F. T. Allen, J. M. Kinser, Proc. of the SPIE, 2824, 84-99, (1996).
  58. “Stabilized Input with a Feedback Pulse-Coupled Neural Network”, J. M. Kinser, J. L. Johnson, Optical Engineering, 35(8), 2158-2161 (1996).
  59. “Optical Syntactic Pattern Recognition Using Fuzzy Scoring”, R. Srinivasan, J. Kinser, M. Schamschula, J. Shamir, H. J. Caulfield, Optics Letters, 21(11) 815-817 (1996).
  60. “Cancer Diagnostics Using Neural Network Sorting of Images”, C. L. Wyman, M. Schreeder, W. Grundy, J. M. Kinser, Proc. of the SPIE, 2760, Orlando, 1996.
  61. “A Simplified Pulse-Coupled Neural Network”, J. M. Kinser, Proc. of the SPIE, 2760, Orlando, 1996
  62. “O(No) pulse-coupled neural network performing humanlike logic”, J. M. Kinser, H. J. Caulfield, Proc. of the SPIE, 2760, Orlando, 1996.
  63. “The Color Pulse-Coupled Neural Network”, J. M. Kinser, presented at the Workshop on Dynamic Neural Networks, Toulouse, France, March 1996
  64. “Object Isolation”, J. M. Kinser and J. L. Johnson, presented at the Workshop on Dynamic Neural Networks, Toulouse, March 1996.
  65. “The Determination of Hidden Neurons”, J. M. Kinser, Optical Memories and Neural Networks, 5 (4), 245-262 (1996).
  66. “Scene evaluation using a pulse-coupled neural network (PCNN)”, Allen, Frank T.; Kinser, Jason M.; Caulfield, H. John, Proc. SPIE, 2565 , 20-30, (1995).
  67. “Optimization Filters Design for GFT by Genetic Algorithm”, H. Peng, H. J. Caulfield, J. M. Kinser, J. M. Hereford, SPIE Proc. 2565 , (1995), 74-84.
  68. “Inability of Higher-Order Outer Product Learning to Map Random Higher-Order Problems”,J. M. Kinser, Neurocomputing 8 , 349-357 (1995).
  69. “Fast Analog Associative Memory”, J. M. Kinser, Proc. SPIE 2568, 290-293 (1995).
  70. “Fractional-Power Synthetic Discriminant Functions”, J. Brasher and J. M. Kinser, Pattern Recognition 27 (4), 577-585 (1994).
  71. “Efficient Code for Optimal Realizable Filter Calculation”, R. D. Juday, J. Kinser, J. L. Alvarez, SPIE Proc. 1959 , (1993).
  72. “Optical Correlator for TOPS”, C. Hester, M. Temmen, R. DeWitt, J. Kinser, J. Brasher, SPIE Proc. 1958(1993).
  73. “Pattern Recognition, Neural Networks and an Optical Commputer for DNA Sequencing and Sequence Analysis”, C. Tibbetts, J. Golden, J. Kinser, (poster) Genome Sequencing and Analysis Conf IV, Hilton Head, SC, Sept, 1992.
  74. “Experimental investigation of simultaneously recorded shadowgraphs and images through a high-velocity turbulent flow”, Haight, Jeffrey S.; Peters, Bruce R.; Kalin, David A.; Brooks, Lori C.; Kinser, Jason M., Proc. SPIE, 1767, 326-334, (1992).
  75. “Landscaping the Correlation Surface”, J. M. Kinser, J. D. Brasher, SPIE Proc. 1701, 188-197 (1992).
  76. “Operational considerations for pattern recognition demonstration for transition of optical processing to systems (TOPS)”Hester, Charles F.; Temmen, Mark G.; Brasher, James D.;Kinser, Jason M.; Dewitt, J. R.; Gregory, Don A. Proc. SPIE, 1701 , 11-16, (1992).
  77. “Layered Optical Processing Architectures”, J. M. Kinser, J. D. Brasher, C. F. Hester, SPIE Proc. 1702, 165-172 (1992).
  78. “Multiordered Mapping Technique for Target Prioritization”, J. M. Kinser, F. Selzer, SPIE Proc. 1699 , 2-7, (1992).
  79. “Optical Processing Architectures for Machine Vision Functions”, J. Brasher, C. Hester, J. Kinser, F. Selzer, M. Temmen, SPIE Proc. 1615, (1991).
  80. “Failure of Fully Correlated Higher Order Neural Nets to Extract Higher Order Information”,J. M. Kinser, SPIE Proc. 1541 (1991).
  81. “Speech Recognition Using Optical Neural Networks”, J. M. Kinser, C. F. Hester, SPIE Proc. 1215 , 408-421 (1990).
  82. (invited paper), “Optical Neural Networks”; H. J. Caulfield, J. M. Kinser, S. K. Rogers, Proc. of the IEEE 77 (10), 1573-1583 (1989).
  83. “Error Correcting Network”, J. M. Kinser, H. J. Caulfield, Proc. of IJCNN, II, 570 (1989).
  84. “A Design for a Massive All-Optical Bidirectional Associative Memory: The Big BAM”, J. M. Kinser, H. J. Caulfield, J. Shamir, Appl. Opt. 27(16), 3442-3444 (1988)

Teaching Interests

CDS 230 – Modeling and Simulation I
CSI 758 – Visualization and Modeling of Complex Systems
Future: Image Operators

Faculty Spotlight on Innovation

Originally published in “Notes of Excellence” newsletter, issue 013 – April 2014.

Jason Kinser is an Associate Professor of Bioinformatics and Computational Biology in the School for Physics, Astronomy, and Computational Sciences, whose current research interest centers around pulse image processing. He is a member of the faculty cohort piloting Mason’s new Active Learning with Technology (ALT) Classroom.

What is the most innovative thing you do with your students and/or your classes? Why do you think it is effective?

The Physics 160+ course provides two innovations that are unique to this course. The first innovation incorporates into the active learning environment a set of interactive physics simulations that pose strenuous problems that require multiple steps to reach the final goal. These problems are difficult for individuals to solve in the allotted time and therefore encourage teamwork to produce an effective solution. Most of the simulations pit the student teams against the computer, but a few are competitive amongst the different teams, adding a bit of fun to the arduous task. The second innovation is the use of mobile technologies in the laboratory settings.

The simulations are effective because they add the element of goal-driven fun into the task of solving the problem. Students enjoy a small reward such as a good “game score” after solving the set of physics problems. The use of mobile technologies tends to open the doors for spontaneous innovations from the students. They are more connected to their smartphones and tablets than expensive lab equipment and can concentrate more on the physics concepts than trying to get unfamiliar equipment to work. Once students see that their mobile technologies are very capable of collecting different types of data, they often suggest other experiments that they could do on their own and at home.

What do you do that creates a strong learning environment for your students?

Fostering student involvement and investigation deeply embeds knowledge and thinking skills. Once the class gets started, we encourage the students to get out of their chairs and discuss their physics problems with other students. This is an effective tool for getting students to think outside of the norms and search for solutions through their own thinking processes as well as using those from several others. Out of this mild chaos, students begin to take on teaching roles, which reinforce their learning. Another effective aspect of this environment is that students can discuss their thoughts about a problem while they are still working through it. This is far more effective than completing a problem on a homework assignment, turning it in, and then learning later if it was correct or not.

What’s one tip that you would offer to faculty new to teaching at Mason?

Faculty who are planning on incorporating active learning in their classrooms should first visit ongoing active classrooms. The preparation for an active learning class is quite different than a lecture course. Active learning does require a tremendous amount of preparation for the first implementation. Faculty should also plan on managing a class without lectures. Instead, they can develop pathways of discovery for the students to follow. Since this is different than the experiences most faculty had when they were students, a visit to an ongoing class can be very enlightening.

What’s the most challenging thing for you in your teaching, and how do you address this challenge?

The biggest challenge was learning how to prepare for the course. Creating student activities seems easy at first, but there always seem to be unforeseen challenges once the students begin working on their tasks. The second challenge is managing the large class. It is important to answer questions quickly, so that students remain engaged and on-task. There are eight tables with a total of 72 seats, and when the students are all working, it becomes quite difficult to answer all of their questions in a timely fashion. This problem is efficaciously addressed by employing two Learning Assistants that also tend to student questions as they arise.

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