Genelle Jenkins
M.S. Biomedical Informatics & Data Science, Arizona State University. ML pipelines, NGS workflows, and production data platforms spanning computational biology, applied statistics, and software engineering.
ML pipelines, genomics tools, and data platforms built for real-world impact.
A DNA-analysis pipeline that reads raw genetic sequencing data and predicts which population group a sample most likely came from, the same type of workflow used in ancestry testing and precision medicine. Built with industry-standard tools (BWA-MEM, GATK) and a Random Forest classifier reaching 79% accuracy and 0.96 AUC.
A deep learning model that watches wearable sensor data and predicts when a Parkinson's patient is about to "freeze" mid-step, a dangerous symptom that causes falls. Converts motion signals into images a neural network can read, reaching 0.85 AUC (vs. 0.83 for a traditional ML baseline).
Built a clinical ML platform for continuous glucose monitor (CGM) data that identifies hypoglycemia risk, explains predictions with SHAP, supports FHIR R4 interoperability (so it can plug into hospital record systems), and includes a validated 49-test data pipeline. Live demo available.
Intimate partner violence (IPV) is a pervasive public health epidemic for pregnant and postpartum people. This study examines optimal methodologies for pediatric healthcare providers to offer education and resources on IPV to new mothers irrespective of disclosure, drawing on qualitative analysis of 150+ clinical interview transcripts to inform evidence-based healthcare intervention design.
Full Publication Details →I am actively seeking Data Scientist, Bioinformatics, Computational Biology, and Health Informatics roles where I can build machine learning pipelines, clinical data platforms, and reproducible analytics systems for healthcare and life sciences teams.