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Researcher
- Andrzej Nycz
- Chris Masuo
- Rama K Vasudevan
- Peter Wang
- Sergei V Kalinin
- Yongtao Liu
- Alex Walters
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
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- Joshua Vaughan
- Kashif Nawaz
- Luke Meyer
- Stephen Jesse
- Udaya C Kalluri
- William Carter
- Akash Jag Prasad
- Amit Shyam
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
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- Calen Kimmell
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- Christopher Fancher
- Christopher Rouleau
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- Clay Leach
- Costas Tsouris
- Debangshu Mukherjee
- Gerd Duscher
- Gordon Robertson
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- J.R. R Matheson
- Jamieson Brechtl
- Jaydeep Karandikar
- Jay Reynolds
- Jeff Brookins
- Jesse Heineman
- Jewook Park
- John Potter
- Jong K Keum
- Kai Li
- Kyle Gluesenkamp
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Mina Yoon
- Neus Domingo Marimon
- Nickolay Lavrik
- Ondrej Dyck
- Radu Custelcean
- Riley Wallace
- Ritin Mathews
- Saban Hus
- Sai Mani Prudhvi Valleti
- Steven Randolph
- Sumner Harris
- Utkarsh Pratiush
- Vincent Paquit
- Vladimir Orlyanchik
- Xiaohan Yang
- 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 invention presents technologies for characterizing physical properties of a sample's surface by combining image processing with machine learning techniques.

An innovative system for automating the surveillance and manipulation of plant tissues using advanced machine vision and robotic tools.

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.