Abstract
The Consortium for Enabling Technologies & Innovation (ETI) was established in 2019 to address emerging technologies within the context of nuclear nonproliferation. ETI creates a research and education environment to support cross-cutting technologies across three core disciplines: 1) computer and engineering science research specifically in a form of machine learning and high performance computing (HPC), 2) advanced manufacturing, and 3) nuclear detection technologies. For outreach and development, ETI hosted the first of three summer schools from August 24-28, 2020 with the theme of “Data Science and Engineering”. The school was hosted in an on-line format and had over 200 participants. The recorded content is available on-line as a resource for students. This describes the hurtles and methods utilized to overcome obstacles limiting in-person workshops in 2020. The summer school had four modules: 1) Fundamentals of data Applications, 2) Computational Machine Learning, 3) Bayesian Modeling and Inference, and 4) Data Science for Safeguards. Modules contained both lectures as well as student exercises. Poll Everywhere was utilized in some modules as an on-line method to engage large groups of students. Data based exercises were also conducted with students to ensure learning objectives were met. Upcoming ETI Summer Schools include Novel Instrumentation in 2021 and Advanced Manufacturing in 2022.
| Original language | English |
|---|---|
| Journal | ASEE Annual Conference and Exposition, Conference Proceedings |
| State | Published - Jul 26 2021 |
| Event | 2021 ASEE Virtual Annual Conference, ASEE 2021 - Virtual, Online Duration: Jul 26 2021 → Jul 29 2021 |
Funding
This material is based upon work supported by the Department of Energy / National Nuclear Security Administration under Award Number(s) DE-NA0003921. This material is based upon work supported by the Department of Energy/National Nuclear Security Administration under Award Number(s) DE-NA0003921. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). LLNL operates under Contract DE-AC52-07NA27344. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).