B.S. Computer Science @ Arizona State University · GPA 3.89/4.00 · Dean's List
I build performance-critical systems across machine learning, quantitative finance, and distributed software. My current interests include deep learning systems, GPU inference, low-latency C++, market microstructure, and open-source infrastructure.
Portfolio • GitHub • LinkedIn • Email
- 🎓 M.S. Computer Science @ University of Southern California — Expected May 2028
- 🎓 B.S. Computer Science @ Arizona State University — May 2026
- ⚙️ Interested in ML systems, GPU computing, compilers, distributed systems, and low-latency C++
- 📈 Building quantitative systems around market microstructure, execution, and stochastic modeling
- 🔬 Experience in research engineering, machine learning, simulation systems, and GPU-backed infrastructure
- 🌱 Currently going deeper into PyTorch internals, Triton/CUDA, LLM inference, and open-source systems work
C++20 · Concurrency · Systems Performance · React
Price-time-priority matching engine built around lock-free MPSC/SPSC queues, cache-line isolation, preallocated data structures, and zero heap allocation on the matching hot path.
- Sustains 2.6M+ orders/sec
- Measures sub-microsecond P99 matching latency
- Parses NASDAQ TotalView-ITCH 5.0
- Implements pre-trade risk controls and exchange order semantics
- Streams live book state to a React/TypeScript visualization
Focus: concurrency, cache behavior, latency, data structures, and reproducible systems benchmarking.
C++20 · Python · FastAPI · WebSockets · React
Real-time market microstructure research and analytics platform built around live order-book data.
- C++ feature engine benchmarked at 1M LOB snapshots/sec
- Computes order-flow imbalance, VPIN, Kyle's λ, realized volatility, and Hawkes-process intensity
- Streams analytics through FastAPI/WebSockets
- Includes Avellaneda–Stoikov and Cartea–Jaimungal market-making models
- Numerical components validated with Python and Catch2 tests
Focus: high-throughput data processing, quantitative research, numerical validation, and real-time systems.
C++20 · Numerical Methods · Python · FastAPI
Options pricing and calibration engine implementing the Heston stochastic-volatility model.
- Carr–Madan FFT pricing with N=4096
- Differential Evolution + Levenberg–Marquardt calibration
- Craig–Sneyd ADI PDE solver for independent validation
- 139/139 Catch2 tests passing
- Interactive web interface for calibration and parameter exploration
Focus: numerical computing, optimization, stochastic models, validation, and C++ performance.
Next.js · TypeScript · Supabase · PostgreSQL · AI APIs
Multimodal task-management application that converts unstructured input into structured workflows.
- Extracts tasks from handwritten notes, images, and voice
- Real-time Kanban synchronization
- PostgreSQL + Supabase Row-Level Security
- Google Calendar OAuth 2.0 integration
- Scheduled reminders and automated deployment through Vercel
Focus: product engineering, multimodal AI integration, distributed application state, and full-stack deployment.
Research framework for evaluating pairs-trading hypotheses using:
- Engle–Granger and Johansen cointegration
- Kalman-filter hedge-ratio estimation
- CVaR and risk-parity portfolio construction
- HAC-adjusted Sharpe ratios
- Monte Carlo simulation
- Out-of-sample validation
The project emphasizes rejecting strategies that fail out-of-sample, rather than optimizing solely for attractive backtest results.
PPO-based market-making agent operating on BTC-USD microstructure state.
- 20-dimensional market-state representation
- Inventory-aware reward formulation
- Avellaneda–Stoikov-inspired objective
- Out-of-sample evaluation across 100 episodes
- Compared against baseline execution strategies
Current interest: making the evaluation framework more rigorous with stronger market-making baselines and statistical confidence intervals.
Deep Learning Systems
├── PyTorch internals
├── Triton / CUDA kernels
├── Transformer inference
├── KV-cache optimization
├── torch.compile / TorchInductor
└── GPU performance profiling
High-Performance Systems
├── C++20
├── Lock-free concurrency
├── Cache-aware design
├── Linux performance
├── Networking
└── Latency benchmarking
Quantitative Systems
├── Market microstructure
├── Execution systems
├── Stochastic modeling
├── Time-series analysis
└── Real-time market data
Languages
C++20 · Python · Rust · C · Java · TypeScript · JavaScript · SQL
ML / Numerical
PyTorch · TensorFlow · NumPy · SciPy · Pandas · Scikit-learn · OpenCV
Systems / Infrastructure
Linux · Docker · Kubernetes · AWS · Git · PostgreSQL · WebSockets
Currently Learning
CUDA · Triton · torch.compile · GPU Profiling · LLM Inference Systems
Research Aide — ASU School of Computing and Augmented Intelligence
Worked on computer-vision and LLM-based methods for biomedical imaging, including architecture benchmarking, failure-mode analysis, and experimental evaluation.
Research Assistant — ASU Biodesign
Developed Rust–Python interfaces for high-performance simulation components and extended simulation tooling with spatial modeling and real-time visualization.
Machine Learning Intern — WDWIL / Magik Kraft
Built TensorFlow/PyTorch defect-detection pipelines and deployed GPU-backed training and inference workloads on AWS using Docker and Kubernetes.
I'm particularly interested in internship and research opportunities involving:
- ML Systems / AI Infrastructure
- GPU / Inference Performance
- C++ Systems Engineering
- Quantitative Development
- Distributed Systems
- Open-Source Infrastructure
If you're working on difficult performance, ML infrastructure, or quantitative systems problems, I'd love to connect.