PNNL

Dr. Eva Brayfindley, PhD, is a senior data scientist and team lead of the Chemical and Nuclear Defense team within the Applied AI Systems group at Pacific Northwest National Laboratory (PNNL). Dr. Brayfindley started at PNNL as an intern, focused on AI for defect detection for spent nuclear fuel in wet storage. Today, she runs a portfolio of AI for nonproliferation projects across the whole alphabet of CBRN (chemical, biological, radiological, and nuclear) challenges. Her current work focuses primarily on statistically driven data science and machine learning for applications in cheminformatics and nuclear safeguards applications, where data is often limited and analysis results need to be highly validated and explainable.

Before her current role at PNNL, Dr. Brayfindley worked as an associate data evaluation officer at the International Atomic Energy Agency in Vienna, Austria, providing statistical analysis support to safeguards inspections within the Safeguards Information Management division.

Dr. Brayfindley received her BS in chemistry from the University of San Francisco in 2014. She then went on to receive her MS and PhD in applied mathematics at North Carolina State University, as well as a graduate certificate in Nuclear Nonproliferation Policy and Technology. Her graduate work, funded through an NNSA university consortium, led her to an early and lasting dedication to all things nonproliferation.

PhD in applied mathematics, North Carolina State University

MS in computational and applied mathematics, North Carolina State University

BS in mathematics, chemistry, University of San Francisco

2021

M. Blumer, C. Chang, E. Brayfindley, J. Nunez, S. Colby, R. Renslow, T. Metz. Journal of Chemical Information and Modeling. Mass Spectrometry Adduct Calculator. doi: 10.1021/acs.jcim.1c00579

J. Nunez, E. Brayfindley, S. Colby, M. McGrady, K.H. Jarman, R. Renslow, T. Metz. "Collision cross section specificity for small molecule identification workflows." arXiv.  https://doi.org/10.48550/arXiv.2111.03134

2020

T. Grimes, E. Church, W. Pitts, L. Wood, E. Brayfindley, L. Erikson, M. Greaves. "Adversarial Training for EM Classification Networks." arXiv. https://doi.org/10.48550/arXiv.2011.10615