Computer Science student at Brooklyn College, Teaching Assistant, and Research Assistant building backend services, real-time data pipelines, and explainable ML applications.
Open to software engineering internships across backend systems, data engineering, AI/ML infrastructure, and FinTech.
| Project | Stack | Description |
|---|---|---|
| Quantitative Trading Framework | Backtrader · QuantStats · yfinance · Plotly Dash | Research benchmark for momentum and mean-reversion strategies across a 4-year backtest with walk-forward validation and transaction costs. Sharpe 0.74. |
| Credit Risk Scoring Engine | XGBoost · SHAP · FastAPI · Docker · Google Cloud Run | Project benchmark for loan-default prediction using 307K records, ROC-AUC 0.7795, a deployed REST API, and explainable SHAP outputs. |
| Fraud Detection Engine | Kafka · Redis · XGBoost · FastAPI · PostgreSQL | Streaming fraud-scoring benchmark with ROC-AUC 0.98, 89% benchmark recall, and 2ms warm scoring latency. Built solo in 8 weeks. |
Metrics are project benchmarks measured in development or evaluation environments, not production guarantees.
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Research Assistant — Brooklyn College
- Built Python ETL and ML workflows with validation, schema enforcement, and anomaly detection.
- Worked on extracting structured data from unstructured narratives.
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Teaching Assistant — Brooklyn College
- Support instruction in cybersecurity, web development, AI foundations, and data science.
- Help students debug projects and translate complex technical concepts into practical solutions.
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Full Stack Software Engineering Intern — InZone Inc.
- Built distributed backend services with Node.js, Express, Docker, and GCP.
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Software Engineering Intern — AutoLake LLC
- Improved API consistency and contributed to security auditing across web applications.
- Building — Quantitative trading systems with walk-forward validation
- Reading — Advances in Financial Machine Learning by Marcos López de Prado
- Exploring — Reliable real-time market-data pipelines with TimescaleDB and kdb+
