Deep Learning for Multi-Messenger Astrophysics: A Gateway for Discovery in the Big Data Era. - Gabrielle Allen; Igor Andreoni; Etienne Bachelet; G. Bruce Berriman; Federica B. Bianco; Rahul Biswas; Matias Carrasco Kind; Kyle Chard; Minsik Cho; Philip S. Cowperthwaite; Zachariah B. Etienne; Daniel George; et al. (CoRR, 2019)

Enabling real-time multi-messenger astrophysics discoveries with deep learning. - Eliu A. Huerta; Gabrielle Allen; Igor Andreoni; Javier Mauricio Antelis; Etienne Bachelet; G. Bruce Berriman; Federica B. Bianco; Rahul Biswas; Matias Carrasco Kind; Kyle Chard; Minsik Cho; Philip S. Cowperthwaite; et al. (CoRR, 2019)

Convergence of artificial intelligence and high performance computing on NSF-supported cyberinfrastructure. - Eliu A. Huerta; Asad Khan; Edward Davis; Colleen Bushell; William D. Gropp; Daniel S. Katz; Volodymyr V. Kindratenko; Seid Koric; William T. C. Kramer; Brendan McGinty; Kenton McHenry; Aaron Saxton (J. Big Data, 2020)

Analysis of User-Support Tickets in the Lifetime of the Blue Waters System. - Gregory H. Bauer; Albert Bode; Brett M. Bode; William T. Kramer; Celso L. Mendes; Aaron Saxton (HUST@SC, 2022)

Deploying a sharded MongoDB cluster as a queued job on a shared HPC architecture. - Aaron Saxton; Stephen Squaire (CoRR, 2022)

Critical Minerals Map Feature Extraction Using Deep Learning. - Shirui Luo; Aaron Saxton; Albert Bode; Priyam Mazumdar; Volodymyr V. Kindratenko (IEEE Geosci. Remote. Sens. Lett., 2023)