Software developer and data analyst working across machine learning, bioinformatics, and scientific computing.
Finishing an M.S. in Computer Science at Georgia Tech, specializing in machine learning, with a B.S. in Biology.
My work combines software development and computational analysis with domain experience in biology, healthcare, ecology, and data-intensive research.
M.S. Computer Science · Georgia Institute of Technology · in progress, ML specialization
B.S. Biology
A.A.S. Software Development · AI Track
Certificate · Python for Data Analytics · AI Track
Languages
Bioinformatics
Libraries & Frameworks
Databases
Methods / Tools
| Project | Description | Tools |
|---|---|---|
| Predictive ML Models - Healthcare, Business, Finance & Agriculture | Six models across logistic regression, decision trees, naive bayes, and neural networks. Predicts heart disease, diabetes likelihood, loan repayment, customer churn, and crop yield from real-world datasets | Scikit-learn · TensorFlow · Python |
| Lōʻihi 16S rRNA Microbiome Analysis | 16S amplicon sequencing analysis of Lōʻihi seamount microbial communities, including sequence QC, SILVA alignment, chimera removal, 97% OTU clustering, taxonomic profiling, and rarefaction analysis | mothur · 16S rRNA · Bioinformatics · Microbial Ecology |
| Ponderosa Pine Climate Regression | Regression analysis of North American conifer field survey data examining how latitude and minimum temperature predict stored energy (AET) in Ponderosa Pines | Python · Excel · Regression Analysis |
| Relational Database Design | Schema design and complete DDL/DML implementation with stored procedures, normalized relational structure, and complex multi-table queries | SQL · T-SQL · PostgreSQL |
| Child Mortality Trends 1900–2018 | Longitudinal analysis across four pediatric age groups over 119 years, with annotated visualizations surfacing the 1918 influenza anomaly against the broader structural decline | Seaborn · Matplotlib |
| Marketing & Product Performance Analysis | End-to-end business analysis - ROI by channel, product segmentation, correlation heatmaps, and spend optimization recommendations derived from historical campaign data | Pandas · Seaborn · Matplotlib |
| Car Dealership Inventory Analysis | Exploratory pricing and inventory analysis covering body type distribution, engine sizing, aspiration type, and curb weight trends using pivot tables and grouped aggregations | Pandas · Matplotlib |
