Novel proposals for FAIR, automated, recommendable, and robust workflows

Funding: This work is partly funded by NSF award OAC-1839900. This material is based upon work supported by the U.S. Department of Energy, Office of Science, under contract number DE-AC02-06CH11357. libEnsemble was developed as part of the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. This research used resources of the OLCF at ORNL, which is supported by the Office of Science of the U.S. DOE under Contract No. DE-AC05-00OR22725.Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2022 lightning talks, which cover five broad topics: data integrity of scientific workflows; a machine learning-based recommendation system; a Python toolkit for running dynamic ensembles of simulations; a cross-platform, high-performance computing utility for processing shell commands; and a meta(data) framework for reproducing hybrid workflows.Postprin

2020 CACR AI/ML Lessons Learned Report

Since Fall of 2019, the Indiana University Center for Applied Cybersecurity Research (CACR) has been exploring the application of machine learning to cybersecurity workflows with the intent of developing the applicable expertise necessary to maintain a commanding lead in the cybersecurity domain where machine learning solutions are expected to increasingly become the norm. In order to serve the objectives laid out in the project charter, CACR primarily worked in partnership with OmniSOC and researchers at Rochester Institute of Technology to explore the application of the ASSERT research prototype to SOC analyst workflows. The intent of this effort was to better understand both the utility of the ASSERT prototype and the challenges associated with the implementation of machine learning approaches to cybersecurity workflows more broadly.Indiana University Center for Applied Cybersecurity Research Indiana University Vice President for IT Indiana University Vice President for Researc

Novel proposals for FAIR, automated, recommendable, and robust workflows

Lightning talks of the Workflows in Support of Large-Scale Science (WORKS) workshop are a venue where the workflow community (researchers, developers, and users) can discuss work in progress, emerging technologies and frameworks, and training and education materials. This paper summarizes the WORKS 2022 lightning talks, which cover five broad topics: data integrity of scientific workflows; a machine learning-based recommendation system; a Python toolkit for running dynamic ensembles of simulations; a cross-platform, high-performance computing utility for processing shell commands; and a meta(data) framework for reproducing hybrid workflows