Introduction

Just Shoeprint Statistics

Just Shoeprint Statistics

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Original Release Date: February 7, 2018

In episode four of our 2018 IPTES Season, Just Science interviews Andrew Kimble III, a student at Albany State University. Mr. Kimble explains his research in how incorporating statistical models in forensic shoeprint analysis can assist in identification. This episode shows how a young forensic professional is finding his own path, along with aiding others in his community.

NIJ and FTCOE are committed to improving the practice of forensic science and strengthening its impact through support of research and development, rigorous technology evaluation and adoption, effective knowledge transfer and education, and comprehensive dissemination of best practices and guidelines to agencies dedicated to combating crime. The future of forensic sciences and its contribution to the public and criminal justice community is a motivating topic to gather expertise in a forum to discuss, learn, and share ideas. The IPTES was specifically designed to bring together practitioners and researchers to enhance information-sharing and promote collaboration among the impression, pattern, and trace evidence analysts, law enforcement, and legal communities.

2018 Impression, Pattern and Trace Evidence Symposium

This episode of Just Science is funded by the National Institute of Justice’s Forensic Technology Center of Excellence [Award 2016-MU-BX-K110].

Listen to or download the episode here:

Guest Biography

Andrew Kimble III is a 20-year-old senior forensic science major from Long Island, New York. He currently attends Albany State University in Albany, Georgia, and his future endeavors will involve working with the FBI in their Crime Scene Department.


The opinions, findings, and conclusions or recommendations expressed in this podcast episode are those of the presenter(s) and do not necessarily reflect those of the U.S. Department of Justice.

Contact us at ForensicCOE@rti.org with any questions and subscribe to our newsletter for notifications.


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