JOSEPH CALUYA

● Investigated The Cancer Genome Atlas (TCGA) data to identify 669 differentially expressed genes potentially involved in breast cancer, 3 of which overlapped with findings from a recent Nature study (Jia et al., 2025) ● Extracted and preprocessed TCGA breast cancer transcriptome and genotype data using common computational sequence tools (BEDTools & VCFtools) ● Ran statistical analyses (Wilcoxon Rank Sum & log-fold change) in R to quantify differential expression in cases vs. controls ● Created data visualizations for quantifying differentiation at breast cancer-associated loci with R

● Led a literature team of 5 members, assigning monthly writing pieces and overseeing publication to DAY magazine/website, resulting in a 22% increase in submissions ● Reviewed and edited 50+ poetry, prose, and multimedia pieces across Spring '24-Spring'25 literary magazines for content, correctness, and clarity, resulting in more cohesive magazines ● Facilitated 5 workshops across Spring/Fall '24 semesters to guide brainstorming & drive 20+ magazine submissions per semester ● Published 15+ poems and prose pieces across 4 volumes of DAY's literary magazine, amplifying Asian-American experiences and storytelling ● Trained and onboarded literature interns on workshop, editing, & submission processes, ensuring a smooth leadership transition ● Cooperated with programming, finance, creative, and multimedia teams to plan weekly meetings and team socials ● Built a reusable CSS template streamlining creation of new article pages by universalizing font, image, headline, and paragraph styles, boosting website engagement by 10% ● Designed editorial-style 3-column section on the DAY USC homepage using HTML/CSS and React.js to display recent literature pieces and news articles ● Revamped About page layout with HTML/CSS, implementing new subsections, fonts, colors, image holders, and icons to more clearly communicate DAY's mission ● Constructed a magazine page in HTML enabling users to peruse a digital copy of the DAY magazine

● Analyzed data from hundreds of breast cancer patients from TCGA program with R and high-performance computing to examine influence of multi-omic characteristics on cancer development ● Collaborated with peers to examine transcriptomics of TCGA ovarian cancer patients with BRCA 1/2 mutations and visualized the spatial distribution of mutations to explain increased survival rates