I care deeply about using science and technology to make healthcare better. I’m also involved in leadership and STEM activities as Co-President of Student Council, Conference Director for Design Tech Model UN, Co-Chair of Girls Who Code, and the Secretary on the City of Redwood City Teen Advisory Board. I’ve presented research at science fairs, presented and published a paper at an IEEE conference, and chaired committees in Model UN. On this website, you’ll be able to see my projects, accomplishments, and interests.
Using mass spectrometry data and machine learning to detect antibiotic resistance faster than traditional methods.
Published and Presented at IEEE CCWC 2026
1st Place, Biological Sciences, Golden Gate STEM Fair
Qualified to California State Science and Engineering Fair
Developed a machine learning diagnostic approach to distinguish ertapenem-resistant and susceptible K. pneumoniae isolates using high-dimensional MALDI-TOF MS data
Built models achieving 86–99% accuracy
Built a complete data preprocessing and analysis pipeline including noise reduction, normalization, spectral alignment, and feature evaluation
Applied data balancing techniques (SMOTE, ADASYN, class-weighting, undersampling)
Assessed performance using stratified cross-validation, confusion matrices, precision, recall, and F1 score
Developed a functional web application for more widespread usage
Presented the research to experts in technology and biology
