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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Andrzej Nycz
- Chris Masuo
- Kashif Nawaz
- Luke Meyer
- Stephen Jesse
- William Carter
- Alexander I Kolesnikov
- Alexei P Sokolov
- Alex Walters
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Bekki Mills
- Benjamin Lawrie
- Bogdan Dryzhakov
- Brian Fricke
- Bruce Hannan
- Chengyun Hua
- Christopher Rouleau
- Costas Tsouris
- Dave Willis
- Debangshu Mukherjee
- Gabor Halasz
- Gerd Duscher
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- Jamieson Brechtl
- Jewook Park
- Jiaqiang Yan
- John Wenzel
- Jong K Keum
- Joshua Vaughan
- Kai Li
- Keju An
- Kyle Gluesenkamp
- Liam Collins
- Loren L Funk
- Luke Chapman
- Mahshid Ahmadi-Kalinina
- Mark Loguillo
- Marti Checa Nualart
- Matthew B Stone
- Md Inzamam Ul Haque
- Mina Yoon
- Neus Domingo Marimon
- Nickolay Lavrik
- Ondrej Dyck
- Peter Wang
- Petro Maksymovych
- Polad Shikhaliev
- Radu Custelcean
- Saban Hus
- Sai Mani Prudhvi Valleti
- Shannon M Mahurin
- Steven Randolph
- Sumner Harris
- Sydney Murray III
- Tao Hong
- Theodore Visscher
- Tomonori Saito
- Utkarsh Pratiush
- Vasilis Tzoganis
- Vasiliy Morozov
- Victor Fanelli
- Vladislav N Sedov
- Yacouba Diawara
- Yun Liu
- Zhiming Gao

This technology provides a device, platform and method of fabrication of new atomically tailored materials. This “synthescope” is a scanning transmission electron microscope (STEM) transformed into an atomic-scale material manipulation platform.

The technology describes an electron beam in a storage ring as a quantum computer.

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

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.

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.
Aromas play a significant role in the quality and safety of food, beverages, and even manufactured products. The ability to detect and interpret these aromas accurately can enhance product safety and consumer satisfaction.