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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Venkatakrishnan Singanallur Vaidyanathan
- Amir K Ziabari
- Diana E Hun
- Kashif Nawaz
- Philip Bingham
- Philip Boudreaux
- Ryan Dehoff
- Stephen Jesse
- Stephen M Killough
- Vincent Paquit
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Benjamin Lawrie
- Bogdan Dryzhakov
- Brian Fricke
- Bryan Maldonado Puente
- Chengyun Hua
- Christopher Rouleau
- Corey Cooke
- Costas Tsouris
- Debangshu Mukherjee
- Gabor Halasz
- Gerd Duscher
- Gina Accawi
- Gs Jung
- Gurneesh Jatana
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- Jamieson Brechtl
- Jewook Park
- Jiaqiang Yan
- John Holliman II
- Jong K Keum
- Kai Li
- Kyle Gluesenkamp
- Liam Collins
- Mahshid Ahmadi-Kalinina
- Mark M Root
- Marti Checa Nualart
- Md Inzamam Ul Haque
- Michael Kirka
- Mina Yoon
- Neus Domingo Marimon
- Nickolay Lavrik
- Nolan Hayes
- Obaid Rahman
- Ondrej Dyck
- Peter Wang
- Petro Maksymovych
- Radu Custelcean
- Ryan Kerekes
- Saban Hus
- Sai Mani Prudhvi Valleti
- Sally Ghanem
- Steven Randolph
- Sumner Harris
- Utkarsh Pratiush
- Zhiming Gao

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

Technologies for optimizing prefab retrofit panel installation using a real-time evaluator is described.

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.