Research

Van de Plas Lab's Research Topics
Our research lies at the interface between (i) mathematical engineering and machine learning; (ii) physical instrumentation and analytical chemistry; and (iii) application domains such as life sciences and medicine. We explore new ways of acquiring, processing, and mining the massive (multi-terabyte) datasets that spectral imaging modalities such as imaging mass spectrometry and other molecular imaging modalities can produce.

Our research commonly involves:

Below you can find a few examples of our output in these areas.

Dimensionality Reduction
 & Unsupervised Machine Learning

Example of Unsupervised Machine Learning: Verbeeck et al., Mass Spectrometry Reviews 39, 3 (2020): pp. 245–291
Example of Unsupervised Machine Learning: Verbeeck et al., Mass Spectrometry Reviews 39, 3 (2020): pp. 245–291

Supervised Machine Learning
 & Explainable Artificial Intelligence

Example of Interpretable Machine Learning: Tideman et al., Analytica Chimica Acta 1177 (2021): p.338522.
Example of Interpretable Machine Learning: Tideman et al., Analytica Chimica Acta 1177 (2021): p.338522.

Multi-modal Analysis, Multi-source Integration, and Image Fusion

Example of Data-Driven Image Fusion: Van de Plas et al., Nature Methods 12, 4 (2015): pp. 366–372.
Example of Data-Driven Image Fusion: Van de Plas et al., Nature Methods 12, 4 (2015): pp. 366–372.

Signal Processing & Registration

Spectral Imaging Modalities
Traditional versus spectral imaging modalities.

Contributions in Mass Spectrometry

Example of Ion Mobility Imaging Mass Spectrometry: Spraggins et al., Analytical Chemistry 91, 22 (2019): pp. 14552–60
Example of Ion Mobility Imaging Mass Spectrometry: Spraggins et al., Analytical Chemistry 91, 22 (2019): pp. 14552–60

Contributions in the Life Sciences — Cancer

Contributions in the Life Sciences — Bacterial Infection & Host-Pathogen Interactions

Contributions in the Life Sciences — Endometriosis

Contributions in the Life Sciences — Tissue Atlases & Other

Building a Human Tissue Atlas: HuBMAP Consortium, Nature 574, 7777 (2019): pp.187-192.
Building a Human Tissue Atlas: HuBMAP Consortium, Nature 574, 7777 (2019): pp.187-192.