BS CS at Arizona State University (graduating May 2026), GPA 3.89, Dean's List. I build low-latency systems and quantitative research tools targeting Quant Developer and Quant Researcher roles at top trading firms. Interests: high-frequency trading infrastructure, statistical arbitrage, and reinforcement learning for market microstructure.
Phoenix, AZ
PPO-trained market-making agent in PyTorch quoting BTC-USD bid/ask spreads from live Coinbase L2 WebSocket data, achieving 2.4× higher mean episode PnL and 8% lower inventory exposure than a TWAP baseline, featuring a Bloomberg-style live trading dashboard.
C++20 price-time priority matching engine processing 2.6M+ orders/sec via lock-free MPSC queue and wait-free SPSC ring buffer, achieving sub-microsecond P99 matching latency with zero heap allocation on the hot path.
Full-stack SaaS application using Next.js, Supabase, and Claude/Gemini Vision APIs to extract structured tasks from handwritten notes, photos, and voice input, featuring real-time Kanban, Google Calendar sync, and automated email reminders.
Production-grade pairs trading backtester in Python using Engle-Granger & Johansen cointegration, Kalman filter dynamic hedge ratios, OU-MLE closed-form estimation, 20+ risk metrics including CVaR/HAC Sharpe, and a 6-tab Streamlit research platform with 163/163 pytest tests passing.
Developer tool that generates and deploys full-stack React/Node.js web applications directly in the browser from natural language prompts, powered by GPT-4/Claude, WebContainers, and Monaco Editor with zero local setup required.
GPA: 3.89/4.00, Dean's List. Relevant coursework: Operating Systems, Data Structures & Algorithms, Distributed Systems, Foundations of Machine Learning, NLP, Image Processing, Software Engineering, Applied Linear Algebra, Probability & Statistics.
Trained and benchmarked computer vision models for biomedical imaging, evaluating LLM-based pipeline integration against established baselines. Reviewed 3–4 papers weekly and wrote technical summaries to guide model selection.
Developed a Rust–Python interface using Maturin for Python orchestration of high-performance simulation components. Built a Django and JavaScript web platform for configuring and visualizing simulation parameters in real time.
Architected a full-stack defect detection system with Django, JavaScript, and Nginx. Deployed scalable ML training and inference pipelines on AWS (EC2, S3, Docker, Kubernetes). Integrated TensorFlow and PyTorch model-serving workflows with CI/CD for production.
C++20, Python, Java, C, JavaScript, TypeScript, SQL
PyTorch, TensorFlow, NumPy, Pandas, SciPy, Statsmodels, Scikit-learn, Streamlit, OpenCV, Gymnasium, React, Next.js, Django, Node.js, Express
Docker, Kubernetes, AWS (EC2, S3), CI/CD, Git, Supabase, PostgreSQL, WebSockets, Multithreading, Lock-free Data Structures
Statistical Arbitrage, Market Microstructure, Reinforcement Learning, Time Series Analysis, Stochastic Calculus, Risk Management