Dr. Christopher Ratto

Electrical and Computer Engineering

Current Courses

Personal Bio

Christopher Ratto is a member of the Senior Professional Staff at The Johns Hopkins University Applied Physics Laboratory (Laurel, Md.)and currently supervises the Algorithm Development and Data Analysis section of the Oceanic, Atmospheric, and Remote Sensing Sciences group. Dr. Ratto's research interests include applications of statistical signal processing and machine learning to problems in remote sensing, target detection, and computer vision.

Education History

  • Bachelor of Electrical Engr Electrical Engineering, The Catholic University of America
  • Master of Science Electrical and Computer Engineering, Duke University
  • Doctor of Philosophy Electrical and Computer Engineering, Duke University

Work Experience

Senior Professional Staff, JHU Applied Physics Laboratory

Publications

C.A. Caceres, M.J. Roos, K.M. Rupp, G. Milsap, N.E. Crone, M.E. Wolmetz, and C.R. Ratto, “Feature Selection Methods for Zero-Shot Learning of Neural Activity,” submitted to Frontiers in Neuroinformatics, 2017.

K. Rupp, M. Roos, G. Milsap, C. Caceres, C. Ratto, M. Chevillet, N. Crone and M. Wolmetz, “Semantic attributes are encoded in human electrocorticographic signals during visual object recognition", NeuroImage, vol. 148, pp. 318-329, January 2017.

Ratto, C.R., Caceres, C.A. and Scheoberlein, H.C., "Cost-Constrained Feature Optimization for Kernel Machine Classifiers," IEEE Signal Processing Letters, 22 (12), vol. 22, no. 12, pp. 2469-2473, December 2015.

Ratto, C. R., Morton, K. D., Collins, L. M., and Torrione, P. A., “Bayesian Context-Dependent Learning for Anomaly Classification in Hyperspectral Imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 4, April 2014.

Ratto, C.R., Beagley, N., Baldwin, K.C., Shipley, K.R., and Sternberger, W.I., “Feature-Based Recognition of Submerged Objects in Holographic Imagery,” Proceedings of the SPIE 9072-4, May 2014.

Ratto, C. R., Morton, K. D., Collins, L.M., and Torrione, P.A.,. “A Bayesian Method for Discriminative Context-Dependent Fusion of GPR-Based Detection Algorithms,” Proceedings of the SPIE, 8357-76, April 2012.

Ratto, C.R, Morton, K.D., McMichael, I.T., Burns, B.P., Clark, W.W., Collins, L.M., and Torrione, P.A, “Integration of LIDAR with the NIITEK GPR for Improved Performance on Rough Terrain,” Proceedings of the SPIE, 8357-67, April 2012.

Morton, K.D, Ratto, C.R, Collins, L.M, and Torrione, P.A, “Change Based Threat Detection in Urban Environments with a Forward Looking Camera,” Proceedings of the SPIE, 8357-59, April 2012.

Ratto, C.R., Torrione, P. A., and Collins, L. M., “Context Dependent Classification: An Approach for Achieving Robust Remote Sensing Performance in Changing Conditions,” in Signal and Image Processing for Remote Sensing, 2nd Edition, CRC Press, February 2012.

Ratto, C.R., Morton, K. D., Collins, L. M., and Torrione, P. A., “A Hidden Markov Context Model for GPR-Based Landmine Detection Incorporating Dirichlet Process Priors”, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Vancouver, BC, July 2011.

Ratto, C.R., Morton, K. D., Collins, L. M., and Torrione, P. A., “A Comparison of Endmember-Based and Principal Components-Based Contextual Learning for Anomaly Classification in Hyperspectral Data”, IEEE 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lisbon, Portugal, June 2011.

Ratto, C., Torrione, P., and Collins, L., “Exploiting Ground-Penetrating Radar Phenomenology in a Context-Dependent Framework for Landmine Detection and Discrimination,” IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 5, pp. 1689-1700, May 2011

Ratto, C. R., Torrione, P. A., Morton, K. D., and Collins, L. M. “Physics-Based Features for Identifying Contextual Factors Affecting Landmine Detection with Ground-Penetrating Radar”, Proceedings of the SPIE, 8017-62, April 2011.

Ratto, C. R., Morton, K. D., Torrione, P. A., and Collins, L. M. “Contextual Learning in Ground-Penetrating Radar Data using Dirichlet Process Priors”, Proceedings of the SPIE, 8017-64, April 2011.

Torrione, P.A., Morton, K.D., Ratto, C.R., Collins, L. M. “Vehicle mounted video-based change detection for novel anomaly detection”, Proceedings of the SPIE, 8017-75, April 2011

Ratto, C. R., Torrione, P. A., and Collins L. M., “Estimation of Soil Permittivity through Autoregressive Modeling of Time-Domain Ground-Penetrating Radar Data", IEEE International Conference on Wireless Information Technology and Systems (ICWITS), Honolulu, HI, August 2010.

Ratto, C., Torrione, P., Morton, K., and Collins L., "Context-Dependent Landmine Detection with Ground-Penetrating Radar using a Hidden Markov Context Model", IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, July 2010.

Ratto, C. R., Torrione, P.A., and Collins L.M., "Context-Dependent Feature Selection Using Unsupervised Contexts Applied to GPR-Based Landmine Detection,” Proceedings of the SPIE, 7664-88, April 2010.

Torrione, P., Ratto, C., and Collins L., “Multiple Instance and Context-Dependent Learning in Hyperspectral Data,” IEEE 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Grenoble, France, August 2009.

Ratto, C. R., Torrione, P. A., and Collins, L. M., “Context-Dependent Feature Selection for Landmine Detection with Ground-Penetrating Radar,” Proceedings of the SPIE, 7303-79, April 2009.

Honors and Awards

  • JHU/APL R.W. Hart Prize for Excellence in R&D, Best Research Project, "Neurally-Integrated Computing" (2015)
  • JHU/APL Publication Award for Outstanding Development Paper in an Externally-Reviewed Journal (2014)
  • James B. Duke Fellow, The Graduate School, Duke University (2007)
  • William H. Gardner, Jr. Fellow, Pratt School of Engineering, Duke University (2007)
  • Tau Beta Pi (2006)

Professional Organizations

IEEE
SPIE
Tau Beta Pi