Computational Biologist specializing in statistical genomics and epigenomics

I am a curious and driven researcher with a knack for asking questions and pushing until I truly understand the answer. I enjoy working through complex problems and finding solutions that make sense, rather than just accepting surface-level explanations. I don’t give up easily, and I’m always drawn to a good challenge. My core passion lies in understanding human disease from a genetic perspective. I build computational pipelines and statistical models to study how DNA methylation, genetic variation, and environmental exposures shape disease risk, with a focus on PTSD, epigenetic biomarkers, oral diseases, and cross-ancestry genomics.

At the same time, my curiosity extends beyond my primary work. I’ve enjoyed exploring plant genetics on the side and regularly dive into new research to stay up to date with developments in genomics. I’m particularly interested in how AI is changing the way we approach scientific problems. I see it as a powerful collaborator that, when used thoughtfully, can significantly accelerate discovery while still requiring creativity, critical thinking, and strong scientific intuition. I genuinely believe we are closer than ever to understanding health and disease at a deeper level—from stem cells to tissue regeneration—and that this progress is being driven by innovation and collaboration.

Outside of research, I maintain a mini garden that has slowly turned into my own experimental space for growing fruits and vegetables!

• EWAS & epigenomics — large-scale epigenome-wide association studies using Illumina EPIC arrays (v1/v2), correcting for genomic inflation and family relatedness
• meQTL mapping — cis-meQTL analyses linking SNPs to methylation at disease-associated CpGs
• Methylation risk scores — cross-ancestry MRS models for PTSD prediction in African cohorts
• Mendelian Randomization — causal inference for periodontal disease, lifestyle factors, and blood metabolites
• ML for biology — deep learning models for intrinsically disordered protein linker prediction

Languages: R · Python · Shell · MATLAB · SPSS
Bioinformatics: minfi · CpGassoc · PLINK · DESeq2 · Bowtie · STAR · FASTQ/BAM/VCF pipelines
Statistics: Mixed-effects models · PCA · Mediation analysis · Logistic/linear regression · Cross-validation
Tools: Git · SSH · VS Code · Tableau

• Charles, R. (2024). Unravelling the Impact of Blood Metabolites and Lifestyle Factors on Periodontal Disease Using Mendelian Randomization. Master's Thesis, University of South Florida. → Read
• PTSD-specific epigenetic biomarkers in the glucocorticoid pathway: distinct DNA methylation at POMC and BGLAP loci (in review)
• Evaluating methylation risk scores for PTSD prediction in an African cohort (in preparation)
• Psychological and epigenetic consequences of exposure to the 1994 Rwandan Genocide against the Tutsi (in preparation)

M.S. Public Health (Computational Genomics) — University of South Florida
B.D.S. — SDM University
Research Assistant, USF (Jan 2025 – Jan 2026) · Graduate Teaching Assistant (2023–2024)

📬 riocx1997@gmail.com · LinkedIn · Ashburn, VA