Shannon Gallagher
Software Engineering Institute Alum
Shannon Gallagher is a data scientist in the SEI’s CERT Division. In that role, Gallagher focuses on modeling, uncertainty quantification, and data visualization and leading research to develop a statistical pipeline to help determine the authenticity of images and videos. Prior to joining the SEI, Gallagher worked as a post-doctoral researcher at the National Institute of Allergy and Infectious Diseases. There she worked on statistical modeling of infectious diseases, competing events analysis for the ACTT-1 COVID-19 trial, and analysis of statistical tests in low event rate settings. Gallagher received a doctoral degree in statistics from Carnegie Mellon University (CMU). While at CMU, Gallagher was a research and teaching assistant and served as president of the Women in Statistics group.
The Myth of Machine Learning Non-Reproducibility and Randomness for Acquisitions and Testing, Evaluation, Verification, and Validation
• Blog Post
By Andrew O. Mellinger , Daniel Justice , Marissa Connor , Shannon Gallagher , Tyler Brooks
3 Recommendations for Machine Unlearning Evaluation Challenges
• Blog Post
By Keltin Grimes , Collin Abidi , Cole Frank , Shannon Gallagher
Assessing LLMs for High Stakes Applications
• White Paper
By Shannon Gallagher , Jasmine Ratchford , Tyler Brooks , Bryan Brown , Eric Heim , Bill Nichols , Scott McMillan , Swati Rallapalli , Carol J. Smith , Nathan M. VanHoudnos , Nick Winski , Andrew O. Mellinger
Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning
• White Paper
By Keltin Grimes , Collin Abidi , Cole Frank , Shannon Gallagher
Tales from the Wild West: Crafting Scenarios to Audit Bias in LLMs
• White Paper
By Katherine-Marie Robinson , Violet Turri , Carol J. Smith , Shannon Gallagher
A Retrospective in Engineering Large Language Models for National Security
• White Paper
By Shannon Gallagher , Andrew O. Mellinger , Jasmine Ratchford , Nick Winski , Tyler Brooks , Eric Heim , Nathan M. VanHoudnos , Swati Rallapalli , William Nichols , Bryan Brown , Angelique McDowell , Hollen Barmer
The Myth of Machine Learning Non-Reproducibility and Randomness for Acquisitions and Testing, Evaluation, Verification, and Validation
• Blog Post
By Andrew O. Mellinger , Daniel Justice , Marissa Connor , Shannon Gallagher , Tyler Brooks