Ondine Biomedical: Data Science & Automation
Data Science Intern, later Data Science Consultant, building automation and analysis tools across antimicrobial research and clinical trial workflows.
Project details and visuals are intentionally generalized to protect confidential and proprietary information.
Checkerboard Assay Automation
Developed and improved automated tools for a high-throughput optical checkerboard assay used in antimicrobial combination research. Automated calculations, interaction classification, quality-control checks, and visualization of assay results, reducing repetitive manual analysis and making results easier to review.
Conceptual diagram: synthetic example, not actual assay data
- High-throughput screening across paired antimicrobial concentrations
- Automated FICI calculation and interaction classification
- Built-in quality-control checks on raw assay output
- Automated visualization of results for faster review
Clinical Trial Data Automation
Built tools to automate the creation, cleaning, transformation, validation, and formatting of clinical research datasets for downstream BioTrial workflows, focused on producing consistent, analysis-ready data while reducing repetitive manual processing.
- Automated cleaning and transformation of clinical trial datasets
- Validation checks to catch formatting and consistency issues early
- Standardized outputs for downstream BioTrial workflows
Laboratory Data Processing & Analysis
Created reusable analytical workflows for laboratory and R&D datasets, including data cleaning, transformation, statistical analysis, and visualization, designed to make scientific data easier to analyze, validate, and communicate.
Workflow Automation
Automated repetitive laboratory and data-management processes, including inventory and batch-record workflows, to reduce manual work and improve consistency and traceability.
Scientific Visualization & Communication
Created clear visualizations and analytical summaries that translated complex laboratory and clinical data into interpretable outputs for scientists and other stakeholders.