Jennifer Newman

Associate Professor
Jennifer Newman

Jennifer Newman has extensive experience in image processing, with her most recent work focusing in forensic applications of micro signals in images. Micro signals include small-magnitude signals such as those introduced when performing steganography, as well as photo-response non uniformity (PRNU) from camera sensors used to link a digital photograph to a device. Classification of images containing such low-magnitude signals require effective machine learning algorithms that can address issues including noise modeling, open-set classification, and data-dependency. Her recent project with the Center for Statistics and Forensic Evidence (CSAFE) produced an extensive image database constructed with mobile phone cameras and steganography apps (https://forensicstats.org/stegoappdb/). Another team project Jennifer is working on uses sophisticated clustering, spatial statistics, and machine learning methodologies to develop a tool to diagnose severe wind events (greater than 50 knots). The data contain a broad variety of sources, from highly-uncertain and low volumes of verified severe wind events, to reliable high-resolution data from model analyses. This NOAA proposal is under review but has been recommended for funding.

Area of Expertise: 
Data Science
Imaging and Signal Processing
Education: 
Ph.D. Mathematics, University of Florida, 1989
Contact
515-294-0302
476 Carver 411 Morrill Rd
Ames
IA
50011-2104
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