Virginia TechBlacksburgBachelor of Science, Computer Science, May 2028May 20283.88 GPA
- Minors: Mathematics, Artificial Intelligence (AI)
- Dean's List: Fall 2024, Spring 2025, Fall 2025, Spring 2026
Education
Skills
| Languages | Python, Java, C, SQL, Bash, Rust |
|---|---|
| Frameworks & Libraries | NumPy, Pandas, React, Streamlit |
| Developer Tools | Git, Bitbucket, GitHub CI/CD, VS Code, Cursor, Eclipse, Jira, Claude Code |
| Data & DevOps | Google BigQuery, Apache Airflow, AWS S3, AWS EC2, Docker, Linux |
Experience
Undergraduate Student Researcher (BURGS Cohort)Virginia Tech Department of Computer Science, Blacksburg, VAAI safety and reward-hacking defenses for MARCO, an autonomous GPU code optimization agent.August 2026 - Present
- Researched AI safety and reward-hacking defenses for MARCO, an autonomous agent system optimizing high-performance GPU code.
- Built Python automated testing pipelines and a 20-strategy adversarial benchmark to measure the reliability of grading safeguards against unfaithful optimizations.
Data And Analytics InternIdentity DigitalBuilt an ML interpretability web app for in-house CatBoost models and streamlined SQL data pipelines.June 2026 - August 2026
- Independently engineered an ML interpretability web application using Python and Streamlit to demystify black-box CatBoost models, visualizing SHAP feature importance across multiple dynamic analytical modes.
- Architected an "Auto Interactions" algorithm utilizing NumPy and Pandas to identify key feature combinations, mathematically converting Tree SHAP values into absolute probability shifts with custom confidence intervals.
- Streamlined data pipelines and processing by pulling high-volume Parquet datasets (100K+ rows) directly from Amazon S3 and revamping complex SQL queries to fulfill ad-hoc marketing reporting requests.
- Collaborated with company executives to support international STEM education, hosting an Anguillan delegation for campus tours and traveling to Anguilla to assist in leading a youth robotics initiative.
Software EngineerStudyGuido via BITS, Blacksburg, VAArchitected the core RAG backend for an AI learning platform, driving semantic textbook ingestion, precision re-ranking, and grounded conversational tutoring.August 2025 - Present
- Engineered a Retrieval-Augmented Generation (RAG) backend for an AI-powered learning platform, utilizing Python, FastAPI, and Amazon S3 to handle textbook ingestion, chunking, and embedding pipelines.
- Optimized context retrieval by implementing a two-stage search pipeline: querying a PostgreSQL (pgvector) database via cosine similarity, then re-ranking results with a BERT Cross-Encoder, improving median context relevance by 33%.
- Co-architected the platform's Mastery module, supplying textbook-grounded RAG context to the Gemini API to facilitate conversational AI tutoring based on the Feynman technique.
- Slashed AI flashcard generation latency by building a PostgreSQL caching layer and spaced-repetition algorithm, reducing LLM dependency while ensuring data persistence with automated cron cleanups.