Significant advancements in scientific computing, Artificial Intelligence (AI), and the hardware and software research environment are enabling researchers to develop automated research workflows (ARWs): building AI and machine learning (ML) as components in the research workflow for data processing and analytics, and using these methods to design and monitor experiments. As stated in a recent report from the National Academies of Sciences, Engineering and Medicine, “the tools and techniques being developed under the large umbrella of ARWs promise to transform the centuries-old serial method of research investigation into processes in which thousands or even millions of simulations or experiments are iterated rapidly in closed loops, with the analysis of data and even the design of experiments or controlled observations being assisted by ML or optimization techniques. Simultaneously, ARWs provide a way to satisfy pressing demands across fields to increase interoperability, reproducibility, replicability, and trustworthiness by better tracking results, recording data, establishing provenance, and creating more consistent metadata than even the most dedicated researchers can provide themselves.”
This colloquium gathers experts who are leading this trend nationwide. They will present their vision as well as their work developing and employing ARWs in astronomy, chemical biology and environmental science. In addition, we will organize a roundtable session with U-M faculty members and the speakers, to discuss the potential of ARWs in various research domains, develop significant ideas and build collaboration to pursue these ideas.
All are welcome to attend the featured presentations on January 26th. Faculty members who are interested in developing automated workflows for your research are strongly encouraged to also attend the January 27th research roundtable.
Registration to attend in advance recommended for both activities.
Michigan Institute for Data ScienceAutomated Research Workflows Colloquium
Michigan Institute for Data Science
January 26-27, 2023
Jan 26, 12:30pm – 5:00pm and Jan 27, 9:00am-12:00pm
Weiser Hall, 10th Floor
Sponsored by: Michigan Institute for Data Science
Contact Information: [email protected]
More Information & Registration
Significant advancements in scientific computing, Artificial Intelligence (AI), and the hardware and software research environment are enabling researchers to develop automated research workflows (ARWs): building AI and machine learning (ML) as components in the research workflow for data processing and analytics, and using these methods to design and monitor experiments. As stated in a recent report from the National Academies of Sciences, Engineering and Medicine, “the tools and techniques being developed under the large umbrella of ARWs promise to transform the centuries-old serial method of research investigation into processes in which thousands or even millions of simulations or experiments are iterated rapidly in closed loops, with the analysis of data and even the design of experiments or controlled observations being assisted by ML or optimization techniques. Simultaneously, ARWs provide a way to satisfy pressing demands across fields to increase interoperability, reproducibility, replicability, and trustworthiness by better tracking results, recording data, establishing provenance, and creating more consistent metadata than even the most dedicated researchers can provide themselves.”
This colloquium gathers experts who are leading this trend nationwide. They will present their vision as well as their work developing and employing ARWs in astronomy, chemical biology and environmental science. In addition, we will organize a roundtable session with U-M faculty members and the speakers, to discuss the potential of ARWs in various research domains, develop significant ideas and build collaboration to pursue these ideas.
All are welcome to attend the featured presentations on January 26th. Faculty members who are interested in developing automated workflows for your research are strongly encouraged to also attend the January 27th research roundtable.
Registration to attend in advance recommended for both activities.
