J Travis Johnston

J Travis Johnston

Postdoctoral Researcher

Bio

Travis Johnston graduated with a Ph. D. in Mathematics in 2014 from the University of South Carolina where he studied problems in extremal combinatorics and graph theory.  His work in machine learning and high performance computing began as a Postdoctoral Researcher at the University of Delaware while working in Michela Taufer's Global Computing Laboratory.  He is currently a Postdoctoral Researcher in Machine Learning and Data Analytics where his focus has been in the design and optimization of deep neural networks at scale.

Publications

Travis Johnston, Steven R. Young, David Hughes, Robert M. Patton, and Devin White. 2017. Optimizing Convolutional Neural Networks for Cloud Detection. In Proceedings of the Machine Learning on HPC Environments (MLHPC'17). ACM, New York, NY, USA, Article 4, 9 pages. DOI: https://doi.org/10.1145/3146347.3146352

Steven R. Young, Derek C. Rose, Travis Johnston, William T. Heller, Thomas P. Karnowski, Thomas E. Potok, Robert M. Patton, Gabriel Perdue, and Jonathan Miller. 2017. Evolving Deep Networks Using HPC. In Proceedings of the Machine Learning on HPC Environments (MLHPC'17). ACM, New York, NY, USA, Article 7, 7 pages. DOI: https://doi.org/10.1145/3146347.3146355

Sebastian M. Cioabă, Willem H. Haemers, Travis Johnston, Matt McGinnis, Cospectral mates for the union of some classes in the Johnson association scheme, Linear Algebra and its Applications, Volume 539, 2018, Pages 219-228, ISSN 0024-3795, https://doi.org/10.1016/j.laa.2017.11.011.

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