I'm a rising senior at the University of Maryland studying Computer Science with a machine learning concentration and a business minor. I'm interested in applied machine learning and financial markets, particularly in systems that produce answers you can trace back to a source. Most of what I build starts with something I wanted to understand better.
Building FastAPI endpoints that query and reprocess court and case management messages for Sonoma County's Integrated Justice System replacement, using Neo4j and parameterized Cypher queries, with payload validation, GUID regeneration, and integration with the existing system.
Engineered a soft bounce email management system designed to protect email credibility, including internal SQL databases and Python scripts that synchronized systems through SendGrid API calls.
Mentored students in the Climate Computing stream of FIRE, supporting research development, data analysis, modeling, and presentation of undergraduate research posters.
Researched relationships between wave activity flux and Arctic weather patterns, writing Python scripts for simulations and analysis on Derecho, the NCAR supercomputer.
A retrieval system that answers operational questions for an HVAC parts distributor. It ingests manufacturer bulletins, regulatory filings, supplier emails, and SOPs into a knowledge base where every answer carries an inline citation to the document it came from. On a 30 question evaluation set, hybrid retrieval reaches 0.97 recall@5 and the agent loop reaches 0.98 citation recall.
An interactive 3D globe that maps the AI supply chain as a layered graph running from power grids and fabs through advanced packaging, data centers, and model labs. Bottlenecks such as CoWoS packaging capacity and grid equipment lead times are modeled directly, so selecting one highlights everything downstream that depends on it. Every node and edge traces to a public, dated source.