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Researcher
- Steve Bullock
- Corson Cramer
- Ali Passian
- Hsuan-Hao Lu
- Joseph Lukens
- Nicholas Peters
- Peeyush Nandwana
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- Alex Plotkowski
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- Costas Tsouris
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- Gyoung Gug Jang
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- Junghoon Chae
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- Nageswara Rao
- Pratishtha Shukla
- Radu Custelcean
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- Sudarsanam Babu
- Sudip Seal
- Sumit Bahl
- Thomas Feldhausen
- Travis Humble
- Yousub Lee
- Aaron Werth
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- Amy Moore
- Andres Marquez Rossy
- Annetta Burger
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- Debangshu Mukherjee
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- Haowen Xu
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- Jong K Keum
- Jordan Wright
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- Jovid Rakhmonov
- Kevin Sparks
- Liangyu Qian
- Liz McBride
- Mariam Kiran
- Md Inzamam Ul Haque
- Michael Kirka
- Mina Yoon
- Nance Ericson
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- Nicholas Richter
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- Pablo Moriano Salazar
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- Varisara Tansakul
- Vimal Ramanuj
- Vipin Kumar
- Vivek Sujan
- Wenjun Ge
- Xiuling Nie
- Ying Yang
- Yiyu Wang

The solution proposed here is a modified carbon-based tile face that is mechanically combined with an insulative backing. The tile face is based on a material architecture to minimize weight and thermal conductivity while maximizing thermal stability.

An ultrabroadband, polarization-entangled photon source for C+L-band quantum networks, enabling adaptive, high-fidelity entanglement distribution.

This technology aims to provide and integrated and oxidation resistant cladding or coating onto carbon-based composites in seconds.

A novel molecular sorbent system for low energy CO2 regeneration is developed by employing CO2-responsive molecules and salt in aqueous media where a precipitating CO2--salt fractal network is formed, resulting in solid-phase formation and sedimentation.

Technologies directed quantum spectroscopy and imaging with Raman and surface-enhanced Raman scattering are described.

Silicon nitride fiber is a critical material mainly used in the aerospace industry but has many applications. This fiber is prized for its properties, as it is transparent to electromagnetic radiation but allows signals to go through it.

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