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Researcher
- Vivek Sujan
- Peeyush Nandwana
- Amit Shyam
- Alex Plotkowski
- Omer Onar
- Srikanth Yoginath
- Adam Siekmann
- Anees Alnajjar
- Blane Fillingim
- Brian Post
- Erdem Asa
- James A Haynes
- James J Nutaro
- Lauren Heinrich
- Pratishtha Shukla
- Rangasayee Kannan
- Sergiy Kalnaus
- Subho Mukherjee
- Sudarsanam Babu
- Sudip Seal
- Sumit Bahl
- Thomas Feldhausen
- Ying Yang
- Yousub Lee
- Alice Perrin
- Ali Passian
- Andres Marquez Rossy
- Beth L Armstrong
- Bruce A Pint
- Bryan Lim
- Christopher Fancher
- Craig A Bridges
- Georgios Polyzos
- Gerry Knapp
- Gordon Robertson
- Harper Jordan
- Hyeonsup Lim
- Isabelle Snyder
- Jaswinder Sharma
- Jay Reynolds
- Jeff Brookins
- Joel Asiamah
- Joel Dawson
- Jovid Rakhmonov
- Mariam Kiran
- Nageswara Rao
- Nance Ericson
- Nancy Dudney
- Nicholas Richter
- Peter Wang
- Ryan Dehoff
- Shajjad Chowdhury
- Sheng Dai
- Steven J Zinkle
- Sunyong Kwon
- Tim Graening Seibert
- Tomas Grejtak
- Varisara Tansakul
- Weicheng Zhong
- Wei Tang
- Xiang Chen
- Yanli Wang
- Yiyu Wang
- Yutai Kato

Here we present a solution for practically demonstrating path-aware routing and visualizing a self-driving network.

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 growing demand for electric vehicles (EVs) has necessitated significant advancements in EV charging technologies to ensure efficient and reliable operation.

The growing demand for renewable energy sources has propelled the development of advanced power conversion systems, particularly in applications involving fuel cells.

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.

We developed and incorporated two innovative mPET/Cu and mPET/Al foils as current collectors in LIBs to enhance cell energy density under XFC conditions.

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.

Digital twins (DTs) have emerged as essential tools for monitoring, predicting, and optimizing physical systems by using real-time data.