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Researcher
- Rama K Vasudevan
- Michael Kirka
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Rangasayee Kannan
- Ryan Dehoff
- Adam Stevens
- Christopher Ledford
- Kashif Nawaz
- Peeyush Nandwana
- Stephen Jesse
- Alice Perrin
- Amir K Ziabari
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Benjamin Lawrie
- Beth L Armstrong
- Bogdan Dryzhakov
- Brian Fricke
- Brian Post
- Chengyun Hua
- Christopher Rouleau
- Corson Cramer
- Costas Tsouris
- Debangshu Mukherjee
- Fred List III
- Gabor Halasz
- Gerd Duscher
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- James Klett
- Jamieson Brechtl
- Jewook Park
- Jiaqiang Yan
- Jong K Keum
- Kai Li
- Keith Carver
- Kyle Gluesenkamp
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Mina Yoon
- Neus Domingo Marimon
- Nickolay Lavrik
- Ondrej Dyck
- Patxi Fernandez-Zelaia
- Petro Maksymovych
- Philip Bingham
- Radu Custelcean
- Richard Howard
- Roger G Miller
- Saban Hus
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Steve Bullock
- Steven Randolph
- Sudarsanam Babu
- Sumner Harris
- Thomas Butcher
- Trevor Aguirre
- Utkarsh Pratiush
- Venkatakrishnan Singanallur Vaidyanathan
- Vincent Paquit
- William Peter
- Yan-Ru Lin
- Ying Yang
- 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 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.

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.