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Researcher
- Amit Shyam
- Alex Plotkowski
- Sam Hollifield
- Yong Chae Lim
- Zhili Feng
- Chad Steed
- James A Haynes
- Jian Chen
- Junghoon Chae
- Mingyan Li
- Peeyush Nandwana
- Rangasayee Kannan
- Ryan Dehoff
- Sumit Bahl
- Travis Humble
- Wei Zhang
- Aaron Werth
- Adam Stevens
- Alice Perrin
- Ali Passian
- Andres Marquez Rossy
- Brian Post
- Brian Weber
- Bryan Lim
- Christopher Fancher
- Dali Wang
- Dean T Pierce
- Emilio Piesciorovsky
- Gary Hahn
- Gerry Knapp
- Gordon Robertson
- Harper Jordan
- Isaac Sikkema
- Jason Jarnagin
- Jay Reynolds
- Jeff Brookins
- Jiheon Jun
- Joel Asiamah
- Joel Dawson
- Joseph Olatt
- Jovid Rakhmonov
- Kevin Spakes
- Kunal Mondal
- Lilian V Swann
- Luke Koch
- Mahim Mathur
- Mark Provo II
- Mary A Adkisson
- Nance Ericson
- Nicholas Richter
- Oscar Martinez
- Peter Wang
- Priyanshi Agrawal
- Raymond Borges Hink
- Rob Root
- Roger G Miller
- Samudra Dasgupta
- Sarah Graham
- Srikanth Yoginath
- Sudarsanam Babu
- Sunyong Kwon
- T Oesch
- Tomas Grejtak
- Varisara Tansakul
- William Peter
- Yarom Polsky
- Ying Yang
- Yiyu Wang
- Yukinori Yamamoto

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

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 ever-changing cellular communication landscape makes it difficult to identify, map, and localize commercial and private cellular base stations (PCBS).

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.

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.

The QVis Quantum Device Circuit Optimization Module gives users the ability to map a circuit to a specific quantum devices based on the device specifications.

QVis is a visual analytics tool that helps uncover temporal and multivariate variations in noise properties of quantum devices.