Peter Mangiafico is a software engineer and product manager in Digital Library Systems and Services in Stanford University Libraries. Peter works on a number of projects, including the Stanford Digital Repository (SDR) and RIALTO (a research intelligence system). Before Stanford, he worked on projects with Visionlearning, the Marine Biological Lab in Woods Hole, MA, Harvard University, and the Advent of Complex Life NASA Astrobiology team. He worked for several years on the Encyclopedia of Life project, and has previously worked as a software engineer, project manager, high school teacher, and NASA researcher.
Peter's professional interests lie in how the science community can become better connected with the public through improved public outreach and education. Since most funding is from public sources, the science community has a large vested interest in engaging in this type of activity. And since the future of our planet depends on appropriate uses of science and technology, the public needs to be informed and become a greater part of the process. Technology such as social media provides opportunities to improve these interactions and reduce the importance of the traditional industries in the middle.
Current Role at Stanford
Product Manager, Revs Project, Digital Library Systems and Services, Stanford Libraries
Education & Certifications
ME, University of Virginia, Engineering Physics (1998)
MS, Johns Hopkins University, Education (1996)
BA, Johns Hopkins University, Physics (1994)
Revs Digital Library, Stanford University
The Revs Digital Library is an online digital archive of automotive history, containing images and documents spanning the history of the automobile. Images are searchable, and users can browse by category or create their own custom galleries.
- Gary Geisler, Web designer, Stanford University
For More Information:
Peter Mangiafico, Torsten Koehler, Jon Merril, Paul McGuire. "United States Patent 6789228 Method and system for the storage and retrieval of web-based education materials", Medical Consumer Media, Sep 7, 2004
Crowdsourcing for metadata enhancement; automated image analysis techniques to enhance metadata; web delivery of digitized content.
Computer vision and image recognition in archaeology
Artificial Intelligence for Data Discovery and Reuse
View details for DOI 10.1145/3359115.3359117
Noise power spectra of images from digital mammography detectors
1999; 26 (7): 1279-1293
Noise characterization through estimation of the noise power spectrum (NPS) is a central component of the evaluation of digital x-ray systems. We begin with a brief review of the fundamentals of NPS theory and measurement, derive explicit expressions for calculation of the one- and two-dimensional (1D and 2D) NPS, and discuss some of the considerations and tradeoffs when these concepts are applied to digital systems. Measurements of the NPS of two detectors for digital mammography are presented to illustrate some of the implications of the choices available. For both systems, two-dimensional noise power spectra obtained over a range of input fluence exhibit pronounced asymmetry between the orthogonal frequency dimensions. The 2D spectra of both systems also demonstrate dominant structures both on and off the primary frequency axes indicative of periodic noise components. Although the two systems share many common noise characteristics, there are significant differences, including markedly different dark-noise magnitudes, differences in NPS shape as a function of both spatial frequency and exposure, and differences in the natures of the residual fixed pattern noise following flat fielding corrections. For low x-ray exposures, quantum noise-limited operation may be possible only at low spatial frequency. Depending on the method of obtaining the 1D NPS (i.e., synthetic slit scanning or slice extraction from the 2D NPS), on-axis periodic structures can be misleadingly smoothed or missed entirely. Our measurements indicate that for these systems, 1D spectra useful for the purpose of detective quantum efficiency calculation may be obtained from thin cuts through the central portion of the calculated 2D NPS. On the other hand, low-frequency spectral values do not converge to an asymptotic value with increasing slit length when 1D spectra are generated using the scanned synthetic slit method. Aliasing can contribute significantly to the digital NPS, especially near the Nyquist frequency. Calculation of the theoretical presampling NPS and explicit inclusion of aliased noise power shows good agreement with measured values.
View details for Web of Science ID 000081515000015
View details for PubMedID 10435530
DQE of a digital mammography detector using cascaded linear systems analysis
RADIOLOGICAL SOC NORTH AMERICA. 1998: 160–160
View details for Web of Science ID 000076659700057
A CCD based digital detector for whole-breast digital mammography
4th International Workshop on Digital Mammography
SPRINGER. 1998: 31–34
View details for Web of Science ID 000079341500005