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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Kashif Nawaz
- Mingyan Li
- Sam Hollifield
- Stephen Jesse
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Benjamin Lawrie
- Bogdan Dryzhakov
- Brian Fricke
- Brian Weber
- Chengyun Hua
- Christopher Rouleau
- Costas Tsouris
- Debangshu Mukherjee
- Gabor Halasz
- Gerd Duscher
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Isaac Sikkema
- Ivan Vlassiouk
- Jamieson Brechtl
- Jewook Park
- Jiaqiang Yan
- Jong K Keum
- Joseph Olatt
- Kai Li
- Kevin Spakes
- Kunal Mondal
- Kyle Gluesenkamp
- Liam Collins
- Lilian V Swann
- Luke Koch
- Mahim Mathur
- Mahshid Ahmadi-Kalinina
- Marti Checa Nualart
- Mary A Adkisson
- Md Inzamam Ul Haque
- Mina Yoon
- Neus Domingo Marimon
- Nickolay Lavrik
- Ondrej Dyck
- Oscar Martinez
- Petro Maksymovych
- Radu Custelcean
- Saban Hus
- Sai Mani Prudhvi Valleti
- Steven Randolph
- Sumner Harris
- T Oesch
- Utkarsh Pratiush
- Zhiming Gao

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.