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2020 ETI Annual Summer School: Data Science and Engineering

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Multidisciplinary Curriculum and Course Development

Tagged Division

Multidisciplinary Engineering

Page Count

12

DOI

10.18260/1-2--36550

Permanent URL

https://peer.asee.org/36550

Download Count

32

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Paper Authors

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Steven R. Biegalski Georgia Institute of Technology

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Steven Biegalski is the Chair of Nuclear and Radiological Engineering and Medical Physics Program at Georgia Institute of Technology. He has three degrees in nuclear engineering from University of Maryland, University of Florida, and University of Illinois, respectively. Early in his career Dr. Biegalski was the Director of Radionuclide Operations at the Center for Monitoring Research. In this position Dr. Biegalski led international efforts to develop and implement radionuclide effluent monitoring technologies. This work supported both US national capabilities and international treaties. Dr. Biegalski was a faculty member at The University of Texas at Austin for 15 years and held the position of Reactor Director for The University of Texas at Austin TRIGA reactor for over a decade. He has advised 25 Ph.D. students to graduation and holds Professional Engineering licenses in the states of Texas and Virginia. Dr. Biegalski's current research focus is on nuclear analytical methods, research isotope production, nuclear forensics, and nuclear non-proliferation.

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Pavel V. Tsvetkov Texas A&M University

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Pavel V. Tsvetkov, Ph.D., is an Associate Professor in the Department of Nuclear Engineering, Texas A&M University. Dr. Tsvetkov’s research program is focused on novel energy systems meeting global growing needs in sustainable resources. The project portfolio includes direct energy conversion, waste minimization efforts, novel reactor designs, instrumentation efforts, and data science and engineering for a broad range of applications targeting optimized designs and performance. He published over 300 papers in peer journals, conference proceedings and reports as well as served as an editor and major contributor for 14 books on energy, environment and nuclear energy.

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Yuguo Tao Georgia Institute of Technology

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Yuguo Tao received B.S. and M.S. from Tianjin University in China, and PhD from University of New South Wales, Sydney, Australia. Prior to joining the Woodruff School of Mechanical Engineering in 2019, Dr. Tao was a Research Scientist at the School of Electrical and Computer Engineering, Georgia Institute of Technology since 2011. Dr. Tao's current works focus on supporting and assisting the overall management, execution and operations of the Consortium for Enabling Technologies and Innovation (ETI), and developing novel instrumentation at Laboratory for Nuclear Nonproliferation and Safety (LANNS).

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Vladimir Sobes University of Tennessee at Knoxville

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Karl Pazdernik Pacific Northwest National Laboratory

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Simon Labov Lawrence Livermore National Laboratory

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Simon Labov is the Program Leader for Nuclear Detection Systems and the Nuclear Security Physics Group Leader at Lawrence Livermore National Laboratory in Livermore, California. Dr. Labov is an expert in nuclear detection systems, advanced spectral and multi-source analysis algorithms, distributed detector systems, and data analytics applied to nuclear threat detection.

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David F. Williams Oak Ridge National Laboratory

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David F. Williams is a PhD chemical engineer with 37 years of professional experience at Oak Ridge National Laboratory and has publications in the nuclear fuel cycle domain spanning reprocessing, fuel fabrication, isotope production, advanced reactor concepts, and nonproliferation/nuclear security. Dave has been a principal investigator or managed projects/programs in each of these applications areas for the DOE Office of Science, Office of Nuclear Energy, Office of Environmental Management and multiple offices within DOE/NNSA.

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James M. Ghawaly Jr. Oak Ridge National Laboratory

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Dr. James Ghawaly Jr. is an applied data scientist in the Advanced Radiation Detection, Imaging, Data Science, and Applications group at Oak Ridge National Laboratory. James has a PhD in nuclear engineering, a MSc in computer engineering, and a BSc in nuclear engineering from the University of Tennessee Knoxville. His research is focused on the application of machine learning, accelerated computing, and other modern data science methods to the field of radiation detection, especially when it comes to anomaly detection in low signal to noise ratio environments. He has also performed fundamental research in the development of training algorithms for deep spiking neural networks.

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Alfred Olivier Hero University of Michigan Orcid 16x16 orcid.org/0000-0002-2531-9670

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Alfred Hero is the John H. Holland Distinguished University Professor of Electrical Engineering and Computer Science and the R. Jamison and Betty Williams Professor of Engineering at the University of Michigan, Ann Arbor. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and the Society for Industrial and Applied Mathematics (SIAM). He is a recipient of the Fourier Award in Signal Processing from the IEEE. He is a Section Editor of the SIAM Journal on Mathematics of Data Science and a Senior Editor of the IEEE Journal on Selected Topics in Signal Processing.

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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.

Biegalski, S. R., & Tsvetkov, P. V., & Tao, Y., & Sobes, V., & Pazdernik, K., & Labov, S., & Williams, D. F., & Ghawaly, J. M., & Hero, A. O. (2021, July), 2020 ETI Annual Summer School: Data Science and Engineering Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. 10.18260/1-2--36550

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