research topics
Currently in Prof. Ishan Barman's Photonics for Quantitative Biology lab at Johns Hopkins University, Dept. of Mechanical Engineering
Monte Carlo Modeling of Spatially Offset Raman Spectroscopy
> Monte Carlo simulations model spatially offset Raman spectroscopy (SORS) in turbid media. This research provides insights into label-free imaging performance across diverse sample geometries, advancing noninvasive diagnostics in complex biological tissues.
1 paper (Analyst, 2024)
1 poster (SciX 2024)
1 award (1st place FACSS Student Poster)
2 works in progress
Spectroscopic Depth-Resolved Mapping of Cartilage Degradation
> SORS and Brillouin microscopy are applied for depth-resolved, label-free analysis of cartilage degradation. This approach holds potential for early osteoarthritis detection and treatment monitoring by providing detailed molecular and mechanical insights.
1 work in progress
Quantitative Phase Imaging of Plasmodium-Infected Mosquito Midguts
> Quantitative phase imaging (QPI) is used to visualize oocysts in malaria-infected mosquito midguts. This label-free imaging method enhances malaria research, offering detailed, high-resolution views of parasite development.
1 work in progress
Quantitative Phase Imaging of Nucleolar Dynamics
> QPI timelapses analyze nucleolar dynamics under anticancer drug-induced stress. This project reveals cellular responses at high resolution, providing new understanding of the nucleolus’s role in cancer treatment.
Quantitative Phase Imaging of Microfluidic Cellular Nanoparticle Delivery
> QPI is integrated with microfluidics to refine nanoparticle delivery into cells. This platform allows precise tracking and optimization of therapeutic delivery mechanisms, improving cellular-level interventions.
Previous work with the AIMS Lab at JHSOM Dept. of Radiology
NMR and MALDI-MS 2D Correlation Spectroscopy
> Statistical heterospectroscopy methods analyze correlations between NMR and MALDI-MS spectra in tumor models. This technique identifies lipid and metabolite interactions, aiding in cancer diagnostic advancements.
4 posters (2023-2024)
1 award (Pre-Clinical Poster Award)
