Turán Problems on Non-Uniform Hypergraphs. - J. Travis Johnston; Linyuan Lu (Electron. J. Comb., 2014)

On the Need for Reproducible Numerical Accuracy through Intelligent Runtime Selection of Reduction Algorithms at the Extreme Scale. - Dylan Chapp; Travis Johnston; Michela Taufer (CLUSTER, 2015)

Performance Tuning of MapReduce Jobs Using Surrogate-based Modeling. - Travis Johnston; Mohammad Alsulmi; Pietro Cicotti; Michela Taufer (ICCS, 2015)

It-Situ Data Analysis of Protein Folding Trajectories. - Travis Johnston; Boyu Zhang 002; Adam Liwo; Silvia Crivelli; Michela Taufer (CoRR, 2015)

Boolean algebras and Lubell functions. - J. Travis Johnston; Linyuan Lu; Kevin G. Milans (J. Comb. Theory A, 2015)

Development of a Scalable Method for Creating Food Groups Using the NHANES Dataset and MapReduce. - Michael R. Wyatt II; Travis Johnston; Mia Papas; Michela Taufer (BCB, 2016)

HYPPO: A Hybrid, Piecewise Polynomial Modeling Technique for Non-Smooth Surfaces. - Travis Johnston; Connor Zanin; Michela Taufer (SBAC-PAD, 2016)

Optimizing Convolutional Neural Networks for Cloud Detection. - J. Travis Johnston; Steven R. Young; David Hughes; Robert M. Patton; Devin White (MLHPC@SC, 2017)

Evolving Deep Networks Using HPC. - Steven R. Young; Derek C. Rose; J. Travis Johnston; William T. Heller; Thomas P. Karnowski; Thomas E. Potok; Robert M. Patton; Gabriel N. Perdue; Jonathan A. Miller (MLHPC@SC, 2017)

Cospectral mates for the union of some classes in the Johnson association scheme. - Sebastian M. Cioaba; Travis Johnston; Matt McGinnis (CoRR, 2017)

In situ data analytics and indexing of protein trajectories. - Travis Johnston; Boyu Zhang 002; Adam Liwo; Silvia Crivelli; Michela Taufer (J. Comput. Chem., 2017)

167-PFlops deep learning for electron microscopy: from learning physics to atomic manipulation. - Robert M. Patton; J. Travis Johnston; Steven R. Young; Catherine D. Schuman; Don D. March; Thomas E. Potok; Derek C. Rose; Seung-Hwan Lim; Thomas P. Karnowski; Maxim A. Ziatdinov; Sergei V. Kalinin (SC, 2018)

Visualization System for Evolutionary Neural Networks for Deep Learning. - Junghoon Chae; Catherine D. Schuman; Steven R. Young; J. Travis Johnston; Derek C. Rose; Robert M. Patton; Thomas E. Potok (IEEE BigData, 2019)

Learning to Predict Material Structure from Neutron Scattering Data. - Cristina Garcia-Cardona; Ramakrishnan Kannan; Travis Johnston; Thomas Proffen; Katharine Page; Sudip K. Seal (IEEE BigData, 2019)

Exascale Deep Learning to Accelerate Cancer Research. - Robert M. Patton; Shahira Abousamra; Dimitris Samaras; Joel H. Saltz; J. Travis Johnston; Steven R. Young; Catherine D. Schuman; Thomas E. Potok; Derek C. Rose; Seung-Hwan Lim; Junghoon Chae; Le Hou (IEEE BigData, 2019)

Evolving Energy Efficient Convolutional Neural Networks. - Steven R. Young; Pravallika Devineni; Maryam Parsa; J. Travis Johnston; Bill Kay; Robert M. Patton; Catherine D. Schuman; Derek C. Rose; Thomas E. Potok (IEEE BigData, 2019)

Fine-Grained Exploitation of Mixed Precision for Faster CNN Training. - Jeremy T. Johnston; Steven R. Young; Catherine D. Schuman; Junghoon Chae; Don D. March; Robert M. Patton; Thomas E. Potok (MLHPC@SC, 2019)

Multi-Objective Optimization for Size and Resilience of Spiking Neural Networks. - Mihaela Dimovska; Travis Johnston; Catherine D. Schuman; J. Parker Mitchell; Thomas E. Potok (UEMCON, 2019)

A Novel Pruning Method for Convolutional Neural Networks Based off Identifying Critical Filters. - Mihaela Dimovska; Travis Johnston (PEARC, 2019)

Early experiences on Summit: Data analytics and AI applications. - David E. Womble; Mallikarjun Shankar; Wayne Joubert; J. Travis Johnston; Jack C. Wells; Jeffrey A. Nichols (IBM J. Res. Dev., 2019)

Creating a portable, high-level graph analytics paradigm for compute and data-intensive applications. - Robert Searles; Stephen Herbein; Travis Johnston; Michela Taufer; Sunita Chandrasekaran (Int. J. High Perform. Comput. Netw., 2019)

Structure Prediction from Neutron Scattering Profiles: A Data Sciences Approach. - Cristina Garcia-Cardona; Ramakrishnan Kannan; Travis Johnston; Thomas Proffen; Sudip K. Seal (IEEE BigData, 2020)

Low Size, Weight, and Power Neuromorphic Computing to Improve Combustion Engine Efficiency. - Catherine D. Schuman; Steven R. Young; J. Parker Mitchell; J. Travis Johnston; Derek C. Rose; Bryan P. Maldonado; Brian C. Kaul (IGSC (Workshops), 2020)

Resilience and Robustness of Spiking Neural Networks for Neuromorphic Systems. - Catherine D. Schuman; J. Parker Mitchell; J. Travis Johnston; Maryam Parsa; Bill Kay; Prasanna Date; Robert M. Patton (IJCNN, 2020)

Accurate and Accelerated Neuromorphic Network Design Leveraging A Bayesian Hyperparameter Pareto Optimization Approach. - Maryam Parsa; Catherine D. Schuman; Nitin Rathi; Amirkoushyar Ziabari; Derek C. Rose; J. Parker Mitchell; J. Travis Johnston; Bill Kay; Steven R. Young; Kaushik Roy 001 (ICONS, 2021)

PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search. - Ariel Keller Rorabaugh; Silvina Caíno-Lores; Michael R. Wyatt II; Travis Johnston; Michela Taufer (CoRR, 2021)

Co-design Center for Exascale Machine Learning Technologies (ExaLearn). - Francis J. Alexander; James A. Ang; Jenna A. Bilbrey; Jan Balewski; Tiernan Casey; Ryan Chard; Jong Choi 001; Sutanay Choudhury; Bert J. Debusschere; Anthony M. DeGennaro; Nikoli Dryden; J. Austin Ellis; et al. (Int. J. High Perform. Comput. Appl., 2021)

Building High-Throughput Neural Architecture Search Workflows via a Decoupled Fitness Prediction Engine. - Ariel Keller Rorabaugh; Silvina Caíno-Lores; Travis Johnston; Michela Taufer (IEEE Trans. Parallel Distributed Syst., 2022)