Portland, Oregon, United States
Currently a Data Science Analyst at the UChicago Center for Computational Medicine and Clinical Artificial Intelligence. I graduated in December 2025 with a Masters in Bioinformatics and Genomics from the University of Oregon. I completed my Bachelors of Science in Biology with a second major in Biomedical Structural Biology from Nanyang Technological University, Singapore in 2024. I aim to help advance personalized healthcare using multiomics data analysis and integration. My research interests include biomarker discovery and validation for clinical prognosis and high-throughput drug response prediction, and computational and systems biology analyses on gene/protein networks to understand mechanisms of health at the molecular level.
Part of the Torcasso Lab @ the Center for Computational Medicine and Clinical Artificial Intelligence.
• Increased the accessibility of histopathology foundation models by wrapping a whole slide Hematoxylin and Eosin (H&E) image processing workflow (TRIDENT) in python with a unified API onto Galaxy. • Spatially resolved the tumor microenvironment (TME) of a triple negative breast cancer (TNBC) clinical cohort by using H&E image-based prediction of gene signatures with a trained attention-based multiple instance learning model and paired analysis of mIHC, and cycIF data. • Routinely presented research in lab meetings, and at outreach events including Knight cancer research day, and a poster at the NCI HTAN conference held in Baltimore.
Bioinformatics workflow design to analyze PacBio MAS-Seq HiFi reads generated unconventionally using Illumina barcodes. ▪ Increased accessibility to novel MAS-Seq data analysis by creating a custom HPC-compatible workflow maximizing the throughput by 15-fold and enabling the use of Illumina primers and reagents instead of the more expensive PacBio Kinnex primer kits for library preparation. ▪ Developed a Nextflow pipeline designed to parallelize the processing of PacBio HiFi 16S data into high-quality amplicon sequence variants using QIIME2 and DADA2. ▪ Increased the quality of taxonomic classification of vineyards by characterizing the metagenome in four hundred vineyard samples to the microbial strain level.
Attached to Computational Biology & Omics Lab under Dr Kumar Selvarajoo • Developed and improved parameters for an evolutionary algorithm-based optimisation of metabolic pathways kinetics. • Improved prediction reliability and industrial applicability by incorporating mechanistic details and exploring underlying mechanisms.