BS CS @ ASU · Graduating May 2026
Phoenix, AZ
CS undergraduate at Arizona State University, graduating May 2026. I spend most of my time studying how markets work and building the systems that interact with them. My work sits at the intersection of quantitative research and low-latency engineering, from reinforcement learning agents and stochastic models to lock-free order books and microstructure signal platforms.
Research-grade real-time microstructure signal platform streaming live Coinbase BTC-USD L2 order book data, computing OFI, VPIN, Kyle's Lambda, and Hawkes process intensity, with a Hamilton-Jacobi-Bellman PDE optimal market-making solver — deployed on a React/FastAPI/Railway stack with a C++ high-performance engine.
Production-grade C++20 options pricing and calibration engine implementing the Heston model via Carr-Madan FFT pricing, Differential Evolution global search, and Levenberg-Marquardt local refinement, achieving sub-basis-point RMSE with 139 Catch2 tests and a live Plotly dashboard.
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.
2022 – May 2026
GPA 3.89 · Dean's List · Graduating May 2026. Coursework: OS, Algorithms, Distributed Systems, ML, NLP, Stochastic Calculus.
Fall 2025
Trained and benchmarked CV and LLM-based models for biomedical imaging pipelines across multiple architectures. Integrated LLM methods into processing pipelines and assessed output quality, identifying failure modes. Reviewed 3–4 papers weekly and authored technical summaries to guide model selection and experimental direction.
Fall 2024 – Present
Built a Rust–Python interface via Maturin for Python orchestration of high-performance simulation components. Implemented a Django + JS platform for real-time configuration and visualization of simulation parameters. Extended the simulation framework to support spatial diffusion modeling, improving biological fidelity.
Summer 2024
Built a full-stack defect detection system with Django and JavaScript as the interface between ML models and production data streams. Deployed training and inference pipelines on AWS (EC2, Docker, Kubernetes) with GPU instances. Integrated TensorFlow and PyTorch model serving with CI/CD for production-grade reliability.
Languages
Frameworks
Systems
Quant