Research,
Teaching,
and Technical Community.
My experience centers on applied machine learning for infectious-disease surveillance, with supporting work in technical education and large-scale AI community programming.
Forward-Looking HPAI Forecasting
Developing a forward-looking, national-scale county-month HPAI forecasting framework for the conterminous United States using environmental, agricultural, and geospatial data streams.
Implementing spatiotemporal machine learning models over county adjacency networks while engineering leakage-safe pipelines with forward-only truncation, forecast-month feature generation, artifact persistence, and automated monthly backtesting.
Designing a decision-support web application with county-level risk visualizations that translate forecast outputs into interpretable biosurveillance insights.
National HPAI Risk Classification
Developed national-scale county-level HPAI risk classification and pseudo-forecasting models using environmental and climate covariates across the conterminous United States.
Achieved 75%+ balanced accuracy on highly imbalanced outbreak data spanning 2022–2024, then presented findings to the USDA ARS Chief Scientist and multiple National Program Leaders.
This internship work became the foundation for a second-author manuscript submitted to Scientific Reports and the ongoing forward-looking forecasting system.
AI Community Programming
Scaled OpenAI’s official Discord community to 850k+ users through technical programming, online events, product-adjacent engagement, and community initiatives.
Supported global user education, technical discussion, and public-facing programming around emerging AI systems.
Operating Systems Instruction
Supported 120+ students through office hours, review sessions, and course support for scheduling, synchronization, deadlocks, memory management, and file-system concepts.