Software Engineer | Data Scientist | Machine Learning
Nashville, Tennessee
View My WorkI'm an experienced software engineer passionate about using data, automated pipelines, and predictive analytics to solve interesting problems. Based in Nashville, Tennessee, I bring a background in computational science and mathematics to every engineering challenge.
After creating full-stack business-critical software and customer-facing applications, I believe in writing well-tested, easily extensible code and in lifelong learning through personal projects and mentoring others. My work spans data engineering, machine learning, and applied mathematics.
I'm always looking to apply my software and analytical skills to tackle new challenges and continue to grow as an engineer.
Listen first. Map the problem domain, stakeholders, and constraints before writing any code.
Design data models, system interfaces, and pipelines with scalability and maintainability in mind.
Iterative development with well-tested code, CI/CD, and frequent feedback loops.
Instrument everything. Use data to validate assumptions and quantify impact.
Continuous improvement through monitoring, retrospectives, and stakeholder alignment.
Selected work and contributions. More on GitHub.
Multi-level machine learning models for differentiating between types of Parkinsonism using diffusion MRI and clinical data. Deployed classification reports as a diagnostic tool for clinicians in collaboration with an international team of neuroscientists.
End-to-end ETL pipeline for collecting, transforming, and visualizing data from project tracking, source control, and build tools. Enabled data-driven decisions to improve code quality, accuracy, and team efficiency.
Research projects at Florida State University investigating downstream impacts of ambiguously condensed data formats and discovering single nucleotide polymorphisms in European water frogs using no reference genome.
Selected engagements with measurable outcomes.
Neurologists struggle to differentiate between types of Parkinsonism using clinical assessment alone, leading to misdiagnosis and delayed treatment for patients.
Built multi-level ML classification models using diffusion MRI and clinical features in collaboration with an international team of neuroscientists and neurologists.
Deployed AID-P, a diagnostic tool that generates classification reports for clinicians, improving diagnostic accuracy for patients with Parkinsonism. Published findings for the research community.
Engineering leadership lacked visibility into team performance, code quality trends, and delivery efficiency across multiple systems and projects.
Designed and built an end-to-end ETL pipeline collecting data from project tracking, source control, and build tools, with automated dashboards for leadership.
Enabled data-driven decisions that improved code quality, estimation accuracy, and team velocity. Adopted company-wide as the standard engineering reporting tool.
A global events marketplace needed to surface relevant events to millions of users across diverse interests and locations with low latency and high accuracy.
Architected and scaled data pipelines and backend systems supporting event discovery, search, and recommendations for the platform's global user base.
Systems served millions of monthly active users with sub-second response times, driving significant increases in event discovery and ticket sales.
Machine learning approach for differentiating between various types of Parkinsonism using diffusion MRI and clinical data, developed in collaboration with an international team of neuroscientists and neurologists.
Florida State University
2013 — 2015
University of Florida
2009 — 2013
Tests express intent more reliably than comments. They prove what the code does, not just what someone hoped it would do.
Seek data before forming hypotheses. The most valuable insights come from questions you didn't think to ask.
The best solution is the one your team can understand, maintain, and extend. Premature abstraction is a liability.
Teaching others scales your knowledge far beyond what you can build alone. The best engineering cultures invest in growing people.
The field moves fast. Stay curious through personal projects, reading, and collaboration. Comfort is the enemy of growth.
I'm always interested in hearing about new opportunities, interesting projects, or connecting with fellow engineers and scientists.