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Emily Herron

Data Science & Engineering


Faculty Advisor: Steven Young

Emily Herron is a Data Science and Engineering Ph.D. candidate in the Bredesen Center at the University of Tennessee, Knoxville, under the guidance of Dr. Steven R. Young. She is also a graduate research assistant in the Learning Systems group at Oak Ridge National Laboratory (ORNL). Her dissertation research involves developing scalable and stable generalized differentiable neural architecture search methods. Her previous research has also involved evolutionary algorithms, neural network ensembles, text mining, and NLP models. She is a member of ACM and IEEE and has served as a reviewer for the International Conference on Machine Learning (ICML) and as a peer mentor in the Bredesen Center. She also earned a B.S. in Computational Science from Mercer University in 2014.


Deep Learning, Neural Architecture Search


B.S. Computational Science, Mercer University, 2018

Professional Service

IMCL Reviewer, Bredesen Center Peer Mentor


Google Scholar


Duncan, J., Fallas, F., Gropp, C., Herron, E., Mahbub, M., Olaya, P., Ponce, E., Samuel, T. K., Schultz, D., Srinivasan, S., Tang, M., Zenkov, V., Zhou, Q., and Begoli, E. (2021). The sensitivity of word embeddings-based author detection models to semantic-preserving adversarial perturbations.
Herron, E. J., Young, S. R., and Potok, T. E. (2020). Ensembles of networks produced from neural architecture search. In Jagode, H., Anzt, H., Juckeland, G., and Ltaief, H., editors, High Performance Computing, pages 223–234, Cham. Springer International Publishing.
Herron, E. J., Young, S. R., and Rose, D. C. (2022). ICDARTS: Improving the Stability of Cyclic DARTS. In 2022 21th IEEE International Conference on Machine Learning and Applications (ICMLA), IEEE.

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