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Researcher
- Rama K Vasudevan
- Sergei V Kalinin
- Yongtao Liu
- Kevin M Roccapriore
- Kyle Kelley
- Maxim A Ziatdinov
- Olga S Ovchinnikova
- Yong Chae Lim
- Kashif Nawaz
- Rangasayee Kannan
- Stephen Jesse
- Adam Stevens
- An-Ping Li
- Andrew Lupini
- Anton Ievlev
- Arpan Biswas
- Bogdan Dryzhakov
- Brian Fricke
- Brian Post
- Bryan Lim
- Christopher Rouleau
- Costas Tsouris
- Debangshu Mukherjee
- Gerd Duscher
- Gs Jung
- Gyoung Gug Jang
- Hoyeon Jeon
- Huixin (anna) Jiang
- Ilia N Ivanov
- Ivan Vlassiouk
- Jamieson Brechtl
- Jewook Park
- Jiheon Jun
- 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
- Peeyush Nandwana
- Priyanshi Agrawal
- Radu Custelcean
- Roger G Miller
- Ryan Dehoff
- Saban Hus
- Sai Mani Prudhvi Valleti
- Sarah Graham
- Steven Randolph
- Sudarsanam Babu
- Sumner Harris
- Tomas Grejtak
- Utkarsh Pratiush
- William Peter
- Yiyu Wang
- Yukinori Yamamoto
- Zhili Feng
- 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.