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Researcher
- Alex Plotkowski
- Amit Shyam
- Peeyush Nandwana
- Blane Fillingim
- Brian Post
- James A Haynes
- Lauren Heinrich
- Sudarsanam Babu
- Sumit Bahl
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- Annetta Burger
- Carter Christopher
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- Costas Tsouris
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- Gs Jung
- Gyoung Gug Jang
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- Jovid Rakhmonov
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- Liz McBride
- Md Inzamam Ul Haque
- Nicholas Richter
- Olga S Ovchinnikova
- Philipe Ambrozio Dias
- Radu Custelcean
- Ramanan Sankaran
- Ryan Dehoff
- Sunyong Kwon
- Taylor Hauser
- Todd Thomas
- Vimal Ramanuj
- Wenjun Ge
- Xiuling Nie
- Ying Yang

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

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.

Among the methods for point source carbon capture, the absorption of CO2 using aqueous amines (namely MEA) from the post-combustion gas stream is currently considered the most promising.

Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.

This work seeks to alter the interface condition through thermal history modification, deposition energy density, and interface surface preparation to prevent interface cracking.

Additive manufacturing (AM) enables the incremental buildup of monolithic components with a variety of materials, and material deposition locations.

Ceramic matrix composites are used in several industries, such as aerospace, for lightweight, high quality and high strength materials. But producing them is time consuming and often low quality.

This innovative approach combines optical and spectral imaging data via machine learning to accurately predict cancer labels directly from tissue images.