I'm a junior at Penn State studying Computer Science and Mathematics, focused on building AI-powered backend systems and data pipelines that solve real problems.

My work sits at the intersection of data engineering, machine learning, and applied AI. I build end-to-end systems with an emphasis on clean architecture, production readiness, and measurable impact.

Currently seeking SWE and AI Engineering co-op opportunities for Fall 2026.

Core: Python, JavaScript, SQL, HTML/CSS
AI/ML: LLM integration, prompt engineering, NLP, scikit-learn, NumPy, pandas, anomaly detection
Backend: FastAPI, REST APIs, Docker, Supabase, PostgreSQL
Frontend: React, Vite
Cloud: AWS (AI Practitioner), Vercel, CI/CD

End-to-end AI pipeline for automated insurance claim decisioning

• Extracts and validates fields from unstructured insurance PDFs using NLP-based parsing and rule-based fraud detection heuristics
• Classifies claims as ACCEPT, FLAG, or REJECT using modular ETL architecture with decoupled parsing, validation, and orchestration layers
• FastAPI backend with REST endpoints for real-time ingestion and retrieval; React dashboard for filtering and reviewing decisions
• Containerized with Docker for cloud-native deployment; validated against 200+ synthetic documents

Python FastAPI Docker React NLP ETL

Real-time threat detection from large-scale log data

• Applies statistical anomaly detection to classify abnormal access patterns and activity spikes as security threats in real time
• Modular ingestion, analysis, and detection layers built for production-scale security monitoring workflows
• FastAPI backend with structured alert output and configurable detection thresholds

Python FastAPI scikit-learn anomaly detection

Systematic backtesting framework for quantitative strategies

• Implements momentum and mean reversion strategies with a custom backtesting engine on historical data
• Evaluates performance across Sharpe ratio, max drawdown, and return profiles
• Modular architecture separating strategy logic, execution simulation, and performance reporting

Python pandas NumPy quantitative finance

The Pennsylvania State University — B.S. Computer Science + B.S. Mathematics (May 2027)
GPA: 3.5 | AWS Certified AI Practitioner (2026)

Coursework: Machine Learning, Database Management, Systems Programming, Data Structures & Algorithms, Probability, Linear Algebra, Discrete Mathematics