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Researcher
- Vivek Sujan
- Adam Siekmann
- Alex Plotkowski
- Amit Shyam
- Omer Onar
- Srikanth Yoginath
- Subho Mukherjee
- Anees Alnajjar
- Chad Steed
- Erdem Asa
- Isabelle Snyder
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- James J Nutaro
- Junghoon Chae
- Peeyush Nandwana
- Pratishtha Shukla
- Sergiy Kalnaus
- Sudip Seal
- Sumit Bahl
- Travis Humble
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- Harper Jordan
- Hyeonsup Lim
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- Jaswinder Sharma
- Jesse McGaha
- Joel Asiamah
- Joel Dawson
- Jovid Rakhmonov
- Kevin Sparks
- Liz McBride
- Mariam Kiran
- Nageswara Rao
- Nance Ericson
- Nancy Dudney
- Nicholas Richter
- Pablo Moriano Salazar
- Rangasayee Kannan
- Ryan Dehoff
- Samudra Dasgupta
- Shajjad Chowdhury
- Sheng Dai
- Sunyong Kwon
- Todd Thomas
- Tomas Grejtak
- Varisara Tansakul
- Xiuling Nie
- Ying Yang
- Yiyu Wang

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

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.

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.