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Researcher
- Amit Shyam
- Beth L Armstrong
- Peeyush Nandwana
- Rama K Vasudevan
- Ryan Dehoff
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- Alex Plotkowski
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- Govindarajan Muralidharan
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- James Gaboardi
- James Haley
- James Klett
- Jamieson Brechtl
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- Jewook Park
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- Peter Wang
- Petro Maksymovych
- Philip Bingham
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- Priyanshi Agrawal
- Radu Custelcean
- Roger G Miller
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- Saban Hus
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- Sergiy Kalnaus
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- Sunyong Kwon
- Taylor Hauser
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- Todd Thomas
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- Weicheng Zhong
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- William Peter
- Xiang Chen
- Xiaobing Liu
- Xiuling Nie
- Yan-Ru Lin
- Yanli Wang
- Yiyu Wang
- Yukinori Yamamoto
- Yutai Kato
- Zhiming Gao

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

Dual-GP addresses limitations in traditional GPBO-driven autonomous experimentation by incorporating an additional surrogate observer and allowing human oversight, this technique improves optimization efficiency via data quality assessment and adaptability to unanticipated exp

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.

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.