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Researcher
- Alex Plotkowski
- Amit Shyam
- Anees Alnajjar
- Srikanth Yoginath
- Yong Chae Lim
- Zhili Feng
- James A Haynes
- James J Nutaro
- Jian Chen
- Nageswara Rao
- Peeyush Nandwana
- Pratishtha Shukla
- Rangasayee Kannan
- Ryan Dehoff
- Sergiy Kalnaus
- Sudip Seal
- Sumit Bahl
- Viswadeep Lebakula
- Wei Zhang
- Aaron Myers
- Adam Stevens
- Alexandre Sorokine
- Alice Perrin
- Ali Passian
- Andres Marquez Rossy
- Annetta Burger
- Beth L Armstrong
- Brian Post
- Bryan Lim
- Carter Christopher
- Chance C Brown
- Clinton Stipek
- Craig A Bridges
- Dali Wang
- Daniel Adams
- Debraj De
- Eve Tsybina
- Femi Omitaomu
- Gautam Malviya Thakur
- Georgios Polyzos
- Gerry Knapp
- Haowen Xu
- Harper Jordan
- James Gaboardi
- Jaswinder Sharma
- Jesse McGaha
- Jessica Moehl
- Jiheon Jun
- Joel Asiamah
- Joel Dawson
- Jovid Rakhmonov
- Justin Cazares
- Kevin Sparks
- Liz McBride
- Mariam Kiran
- Matt Larson
- Nance Ericson
- Nancy Dudney
- Nicholas Richter
- Philipe Ambrozio Dias
- Priyanshi Agrawal
- Roger G Miller
- Sarah Graham
- Sheng Dai
- Sudarsanam Babu
- Sunyong Kwon
- Taylor Hauser
- Todd Thomas
- Tomas Grejtak
- Varisara Tansakul
- William Peter
- Xiuling Nie
- Ying Yang
- Yiyu Wang
- Yukinori Yamamoto

The eDICEML digital twin is proposed which emulates networks and hosts of an instrument-computing ecosystem. It runs natively on an ecosystem’s host or as a portable virtual machine.

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.

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.

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

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.