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Researcher
- Ilias Belharouak
- Rama K Vasudevan
- Ryan Dehoff
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Alexey Serov
- Ali Abouimrane
- Jaswinder Sharma
- Kashif Nawaz
- Marm Dixit
- Michael Kirka
- Ruhul Amin
- Stephen Jesse
- Vincent Paquit
- Xiang Lyu
- Adam Stevens
- Ahmed Hassen
- Alex Plotkowski
- Alice Perrin
- Amir K Ziabari
- Amit K Naskar
- Amit Shyam
- An-Ping Li
- Andres Marquez Rossy
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Benjamin Lawrie
- Ben LaRiviere
- Beth L Armstrong
- Blane Fillingim
- Bogdan Dryzhakov
- Brian Fricke
- Brian Post
- Chengyun Hua
- Christopher Ledford
- Christopher Rouleau
- Clay Leach
- Costas Tsouris
- David L Wood III
- David Nuttall
- Debangshu Mukherjee
- Gabor Halasz
- Gabriel Veith
- Georgios Polyzos
- Gerd Duscher
- Gs Jung
- Gyoung Gug Jang
- Holly Humphrey
- Hongbin Sun
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- James Haley
- James Szybist
- Jamieson Brechtl
- Jewook Park
- Jiaqiang Yan
- Jonathan Willocks
- Jong K Keum
- Junbin Choi
- Kai Li
- Khryslyn G Araño
- Kyle Gluesenkamp
- Liam Collins
- Logan Kearney
- Lu Yu
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Meghan Lamm
- Michael Toomey
- Michelle Lehmann
- Mina Yoon
- Nance Ericson
- Neus Domingo Marimon
- Nickolay Lavrik
- Nihal Kanbargi
- Ondrej Dyck
- Patxi Fernandez-Zelaia
- Paul Groth
- Peeyush Nandwana
- Petro Maksymovych
- Philip Bingham
- Pradeep Ramuhalli
- Radu Custelcean
- Rangasayee Kannan
- Ritu Sahore
- Roger G Miller
- Saban Hus
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Steven Randolph
- Sudarsanam Babu
- Sumner Harris
- Todd Toops
- Utkarsh Pratiush
- Venkatakrishnan Singanallur Vaidyanathan
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Xiaobing Liu
- Yan-Ru Lin
- Yaocai Bai
- Ying Yang
- Yukinori Yamamoto
- Zhijia Du
- 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.