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Researcher
- Andrzej Nycz
- Amit Shyam
- Beth L Armstrong
- Chris Masuo
- Peeyush Nandwana
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- Jun Qu
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- Wei Zhang
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- Yousub Lee
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- Alexandre Sorokine
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- Annetta Burger
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- Bruce A Pint
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- Carter Christopher
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- Christopher Ledford
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- Clay Leach
- Clinton Stipek
- Dali Wang
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- Debraj De
- Ethan Self
- Eve Tsybina
- Gabriel Veith
- Gautam Malviya Thakur
- Gerry Knapp
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- Gordon Robertson
- Govindarajan Muralidharan
- J.R. R Matheson
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- Jaydeep Karandikar
- Jay Reynolds
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- Jesse McGaha
- Jessica Moehl
- Jiheon Jun
- John Potter
- Jordan Wright
- Jovid Rakhmonov
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- Khryslyn G Araño
- Liz McBride
- Marm Dixit
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- Michael Kirka
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- Nicholas Richter
- Philipe Ambrozio Dias
- Priyanshi Agrawal
- Riley Wallace
- Ritin Mathews
- Roger G Miller
- Rose Montgomery
- Sarah Graham
- Sergiy Kalnaus
- Shajjad Chowdhury
- Steven J Zinkle
- Sunyong Kwon
- Taylor Hauser
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- Tim Graening Seibert
- Todd Thomas
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- Yukinori Yamamoto
- Yutai Kato

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

A finite element approach integrated with a novel constitute model to predict phase change, residual stresses and part deformation.

Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modelling building height at scale are hindered by two pervasive issues.

Currently available cast Al alloys are not suitable for various high-performance conductor applications, such as rotor, inverter, windings, busbar, heat exchangers/sinks, etc.

The invented alloys are a new family of Al-Mg alloys. This new family of Al-based alloys demonstrate an excellent ductility (10 ± 2 % elongation) despite the high content of impurities commonly observed in recycled aluminum.

System and method for part porosity monitoring of additively manufactured components using machining
In additive manufacturing, choice of process parameters for a given material and geometry can result in porosities in the build volume, which can result in scrap.

The lack of real-time insights into how materials evolve during laser powder bed fusion has limited the adoption by inhibiting part qualification. The developed approach provides key data needed to fabricate born qualified parts.

This invention is directed to a machine leaning methodology to quantify the association of a set of input variables to a set of output variables, specifically for the one-to-many scenarios in which the output exhibits a range of variations under the same replicated input condi

A new nanostructured bainitic steel with accelerated kinetics for bainite formation at 200 C was designed using a coupled CALPHAD, machine learning, and data mining approach.