Kayvan Samimi

This paper demonstrates that autofluorescence lifetime imaging can identify immune cell subsets and activation states in PBMCs with high accuracy, enabling label-free metabolic analysis.

This paper introduces a photon pooling technique for faster single-cell FLIM analysis without machine learning, achieving single-cell metabolic data every second and 15x faster mosaic acquisition.

FLIM Playground provides an interactive, open-source platform for analyzing single-cell fluorescence lifetime data, offering unified workflows and real-time analysis tools validated on cancer cell line datasets.

The paper develops an automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids, achieving high accuracy and significantly faster processing than manual methods.

This paper introduces a TCSPC-based flow cytometer for real-time single-cell autofluorescence lifetime analysis, demonstrating viability and sensitivity to metabolic changes in various cell types.