With a powerful blend of programming expertise and extensive wet-lab experience in genomics and proteomics, I am uniquely positioned to bridge the gap between laboratory science and computational analysis. Over three years as a Genomics Postdoctoral Fellow at the College of Computer Science and Engineering, University of South Carolina, I have led five machine learning projects — advancing from decision trees in Scikit-learn to sophisticated neural networks using PyTorch.
Proficient in R for statistical computing and bioinformatics, leveraging the Bioconductor ecosystem for genomic data analysis.
Advanced Python scripting for machine learning, structural biology, and sequence analysis pipelines.
Proficient in text-based environments and shell scripting for pipeline automation and high-performance computing workflows.
Extensive wet-lab sequencing experience combined with programming expertise enables seamless integration of raw data into meaningful insights — alignments, quality control, and advanced phylogenetic analysis.
RNA-Seq analysis pipelines from raw reads through differential expression, pathway enrichment, and biological interpretation. Includes DESeq2-based factorial modeling and circadian transcriptomics.
Genome-wide methylation profiling to explore epigenetic changes and their influence on health. Applied Random Forest models to CpG loci data to identify biomarkers of cognitive impairment.
An all-in-one pipeline from gene and construct design to structural analysis of macromolecules. Developing comprehensive tools bridging biology and computation using PyMOL, VMD, and GROMACS.
Deep understanding of PCR techniques combined with advanced programming skills enables custom primer and assay design for innovative diagnostic products, services, and research applications.
Led five ML projects in biological research, from classical decision trees to deep neural networks, applied to genomics, proteomics, and medical imaging.
Available for workshops and hands-on training sessions in computational biology, bioinformatics, and data analysis for research groups, core facilities, and industry teams.
Hands-on introduction to R and Bioconductor for RNA-Seq analysis, from data loading and quality control through differential expression and visualization.
Practical workshop on Python and Biopython for sequence analysis, data manipulation, and building bioinformatics pipelines.
Introduction to machine learning approaches applicable to biological data — classification, feature selection, and interpretable models for omics datasets.
Interested in a workshop or collaboration? Get in touch. Visit my GitHub for code repositories and the Data Analysis in R 2024 resource.