Anh Tran

Scientific Machine Learning

Author profile picture

Scientific Machine Learning

anhtran@sandia.gov

Sandia National Laboratories, New Mexico
P.O. Box 5800
Albuquerque, NM 87185-1323

Biography

Anh joined Sandia as a postdoctoral appointee in 2019 and became a staff member at the Center of Computing Research since 2020. He has a wide research interest, including optimization, machine learning, and uncertainty quantification methodologies for multiscale computational materials science applications.

Education

Ph.D., Mechanical Engineering, Georgia Institute of Technology, Dec 2018.

M.S., Mechanical Engineering, Georgia Institute of Technology, May 2018.

M.S., Mathematics, Georgia Southern University, May 2014.

B.S., Mechanical Engineering, Georgia Institute of Technology, Dec 2011.

Publications

Chuck Randall Smallwood, Haley Monteith, Stephanie Dawn Kolker, Jason Paul Sammon, Philip Rocco Miller, Jenna Young Schambach, James Bryce Ricken, Katey Anthony, Nicholas Hasson, Chris Maio, Andr Pellerin, Eli Miller, Sivan Orit, Effie Eliani, Tyler Jones, Kevin Rozmiarek, Jessica Kustas, Tessily Nicholle Hogancamp, (2022). Increasing Biological Fidelity of Arctic Greenhouse Gas Emissions from Permafrost to Improve Climate Modeling AGU Fall Meeting Document ID: 1688067

Patrick Knapp, (2022). Compact Pulsed Power driven x-ray sources for HED experiments Ndwg Document ID: 1688079

David Lee Damm, (2022). Microscale Modeling of Energetic Materials with Strength & Plasticity Informed by Molecular Dynamics International Conference on Plasticity, Strength, and Damage 2023 Document ID: 1676066

Sarah Albert, J.MarkHale(UniversityofUtah), KristinePankow(UniversityofUtah), (2022). Single-Channel Infrasound Detection Using Machine Learning 2023 Seismological Society of America Annual Meeting Document ID: 1677874

Michael Christopher Darling, Richard V. Field, Mark A. Smith, JasonW. Boada, J.Eric Bickel, Justin E Doak, James McKeather Headen, ZacharyJ. Smith, David John Stracuzzi, (2022). Decision Science for Machine Learning (DeSciML) https://www.osti.gov/search/identifier:1903086 Document ID: 1640759

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Software

Dakota