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MTC envelope: defining the capability of large scale computers in the context of parallel scripting applications., , , , and . HPDC, page 37-48. ACM, (2013)Portable and Reusable Deep Learning Infrastructure with Containers to Accelerate Cancer Studies., , , , , and . ESPM2@SC, page 54-61. IEEE, (2018)Implementing Reliable Data Structures for MPI Services in High Component Count Systems., , , , , and . PVM/MPI, volume 5759 of Lecture Notes in Computer Science, page 321-322. Springer, (2009)CANDLE/Supervisor: a workflow framework for machine learning applied to cancer research., , , , , , , , , and 3 other author(s). BMC Bioinform., 19-S (18): 59-69 (2018)High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow., , , , , , , , , and . BMC Bioinform., 19-S (18): 81-97 (2018)In-situ workflow auto-tuning through combining component models., , , , , and . PPoPP, page 467-468. ACM, (2021)Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications., , , , , and . SC, page 28. ACM, (2021)Braid-DB: Toward AI-Driven Science with Machine Learning Provenance., , , , , and . SMC, volume 1512 of Communications in Computer and Information Science, page 247-261. Springer, (2021)An Automation Framework for Comparison of Cancer Response Models Across Configurations., , , , , , and . e-Science, page 1-10. IEEE, (2023)Tracking Dubious Data: Protecting Scientific Workflows from Invalidated Experiments., , and . e-Science, page 456-461. IEEE, (2022)