About
Welcome
I am a Ph.D. candidate in Physics at the University of Georgia, working with Prof. Yiping Zhao. My research focuses on surface-enhanced Raman spectroscopy (SERS), plasmonic nanomaterials, silver nanorod substrates, and machine learning-assisted spectroscopy for chemical and biological sensing.
My work combines nanofabrication, Raman spectroscopy, data analysis, and predictive modeling to develop rapid, label-free sensing approaches. I am especially interested in using SERS and machine learning for virus discrimination, bacterial identification, spectral variability analysis, and quantitative prediction from complex Raman spectra.
Education
- Ph.D. in Physics, University of Georgia, USA
- M.S. in Solid State Materials, Indian Institute of Technology Delhi, India
Research Interests
My current research is centered on four main areas:
- SERS-based biosensing: label-free detection and classification of viruses and bacterial samples using Raman spectral fingerprints.
- Plasmonic nanomaterials: fabrication and optimization of silver nanorod substrates for enhanced Raman signal generation.
- Machine learning for spectroscopy: classification, regression, clustering, and model validation for Raman and SERS datasets.
- Spectral variability and reproducibility: understanding how substrate structure, molecular orientation, concentration, and measurement conditions influence SERS spectral shape.
Selected Projects
Label-free SERS and machine learning for virus detection
Developing SERS-based approaches combined with machine learning models for rapid classification and quantification of bovine respiratory disease complex viruses.
Bacterial supernatant analysis using SERS
Using Ag nanorod-based SERS substrates and multivariate analysis to distinguish bacterial supernatant samples from different organisms and growth media.
Spectral shape variability in SERS
Studying how substrate optics, molecular orientation, analyte concentration, and time-dependent adsorption affect SERS spectral features.
Spectroscopy data analysis tools
Developing Raman/SERS data-processing workflows, including baseline correction, spectral preprocessing, visualization, and model evaluation.
News
- March 2026 — Presented an oral talk at ACS Spring 2026 on machine learning-enhanced airPLS parameter optimization for noise-robust SERS baseline correction.
- March 2026 — Served as a presider for the “Advances in Spectroscopic Detection” session under the Division of Analytical Chemistry at ACS Spring 2026.
- March 2026 — Presented two Sci-Mix posters at ACS Spring 2026.
- March 2026 — Conference paper “SpectraGuru: a community-guided path toward scalable Raman and SERS analysis” was published in SPIE Proceedings.
- March 2026 — Submitted a manuscript on bacterial SERS analysis for journal review.