Aviation Applications Program (AAP)
I co-manage the Turbulence Remote Sensing Project funded by the
FAA's Aviation Weather Research program. Currently, our primary
effort is to develop and implement a turbulence detection product
based on data from NEXRAD radars. A web-based display tool providing
real-time, 3-D depiction of in-cloud turbulence for a region around
Chicago will be operationally demonstrated beginning in Summer 2005.
I am also a member of the American Meteorological Society's Committee on Artificial Intelligence Applications to Environmental Science.
B.A., Physics, Swarthmore College, 1990
M.A., Mathematics, University of Colorado, 1996
M.S., Applied Mathematics, University of Colorado, 1998
Ph.D., Mathematics, University of Colorado, 2000
Williams, J. K., L. Cornman, D. Gilbert, S. G. Carson, and J. Yee,
2004: Improved remote detection of turbulence using ground-based
Doppler radars. AMS 11th Conference on Aviation, Range, and Aerospace
Meteorology, CD-ROM, 4.5.
Williams, J. K., J. Vivekanandan and G. Zhang, 2003: Enhanced dual-wavelength technique for remote detection of cloud liquid water content. AMS 31st Conference on Radar Meteorology, 130-133.
Williams, J. K., J. Vivekanandan, and G. Zhang, 2002: Evaluation of remote icing detection techniques using X-, Ka-, and W-band radar and microwave radiometer observations. 10th Conference on Aviation, Range, and Aerospace Meteorology, 224-227.
Cornman, L. B., S. Gerding, G. Meymaris, and J. Williams, 2002: Evaluation of an airborne radar turb-ulence detection algorithm. 10th Conference on Aviation, Range, and Aerospace Meteorology, 237-240.
Cornman, L. B., J. K. Williams and R. K. Goodrich, 2000. The detection of convective turbulence using airborne Doppler radars. Ninth Conference on Aviation, Range, and Aerospace Meteorology.
Williams, J. K., 2000: On the Convergence of Model-free Policy Iteration Algorithms for Reinforcement Learning: Stochastic Approximation under Discontinuous Mean Dynamics. Doctoral Dissertation, University of Colorado.
Williams, J. K. and S. Singh, 1999: Experimental results on learning stochastic memoryless policies for partially observable Markov decision processes. In M. S. Kearns, S. A. Solla and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11, MIT Press, 1073-1079.