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Researcher
- Amit Shyam
- Rama K Vasudevan
- Ryan Dehoff
- Sergei V Kalinin
- Yongtao Liu
- Alex Plotkowski
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Alice Perrin
- James A Haynes
- Kashif Nawaz
- Michael Kirka
- Stephen Jesse
- Sumit Bahl
- Vincent Paquit
- Ying Yang
- Adam Stevens
- Ahmed Hassen
- Amir K Ziabari
- An-Ping Li
- Andres Marquez Rossy
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Benjamin Lawrie
- Blane Fillingim
- Bogdan Dryzhakov
- Brian Fricke
- Brian Post
- Chengyun Hua
- Christopher Fancher
- Christopher Ledford
- Christopher Rouleau
- Clay Leach
- Costas Tsouris
- David Nuttall
- Dean T Pierce
- Debangshu Mukherjee
- Gabor Halasz
- Gerd Duscher
- Gerry Knapp
- Gordon Robertson
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- James Haley
- Jamieson Brechtl
- Jay Reynolds
- Jeff Brookins
- Jewook Park
- Jiaqiang Yan
- Jong K Keum
- Jovid Rakhmonov
- Kai Li
- Kyle Gluesenkamp
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Mina Yoon
- Neus Domingo Marimon
- Nicholas Richter
- Nickolay Lavrik
- Ondrej Dyck
- Patxi Fernandez-Zelaia
- Peeyush Nandwana
- Peter Wang
- Petro Maksymovych
- Philip Bingham
- Radu Custelcean
- Rangasayee Kannan
- Roger G Miller
- Saban Hus
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Steven Randolph
- Sudarsanam Babu
- Sumner Harris
- Sunyong Kwon
- Utkarsh Pratiush
- Venkatakrishnan Singanallur Vaidyanathan
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Xiaobing Liu
- Yan-Ru Lin
- Yukinori Yamamoto
- Zhiming Gao

In scientific research and industrial applications, selecting the most accurate model to describe a relationship between input parameters and target characteristics of experiments is crucial.

This technology combines 3D printing and compression molding to produce high-strength, low-porosity composite articles.

This invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.

Simurgh revolutionizes industrial CT imaging with AI, enhancing speed and accuracy in nondestructive testing for complex parts, reducing costs.

This invention introduces a system for microscopy called pan-sharpening, enabling the generation of images with both full-spatial and full-spectral resolution without needing to capture the entire dataset, significantly reducing data acquisition time.

An innovative low-cost system for in-situ monitoring of strain and temperature during directed energy deposition.

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

This technology introduces an advanced machine learning approach for enhancing chemical imaging by correlating data from two mass spectrometry imaging (MSI) techniques.