Research

Writing research papers is one of my hobbies. My interests span AI engineering, multi-agent systems, large language models, and algorithmic trading. I try to write at least one paper a year, focusing on practical applications of AI in finance.

Research Papers

Portfolio Optimization Using Monte Carlo Methods and Artificial Neural Networks

Exploring the intersection of traditional quantitative finance methods with modern neural network approaches for portfolio construction and optimization.

Quantitative Finance Neural Networks Monte Carlo

Deep Reinforcement Learning for Algorithmic Trading in Financial Markets

Applying deep reinforcement learning techniques to build autonomous trading agents capable of operating in financial markets.

Reinforcement Learning Algorithmic Trading AI Agents

Evaluating Large Language Models on the Performance of Routine Programming Tasks

A systematic evaluation of LLM capabilities and limitations when applied to everyday software development tasks.

LLMs Code Generation AI Evaluation

Predictive Power in ETF Markets: Leveraging Artificial Intelligence for ETF Price Forecasting

Using AI techniques to predict ETF price movements, drawing on experience building electronic trading systems for ETF market making desks.

ETFs AI Price Forecasting

Citadel Securities: Seizing the Blockchain at the Speed of Light

Examining blockchain technology through the lens of high-frequency trading infrastructure. Written for the Harvard Business Analytics Program.

Blockchain HFT Market Structure

Coin: Secure Mobile and Online Payments for Small and Medium Enterprises

A proposal for a mobile payments platform designed to reduce transaction costs for small businesses.

Fintech Payments Mobile

Using Spinnaker to Manage Deployments and Infrastructure in AWS

Documenting production patterns for continuous delivery automation across a fleet of JavaScript applications.

DevOps Spinnaker Node.js

Research Interests

AI Engineering & Production AI Systems

  • Full-stack AI orchestration using LangGraph.js and TypeScript
  • Modular agent architectures for enterprise applications
  • Recursive language models and long-context processing
  • Observability and tracing in production AI systems
  • Edge computing and distributed AI deployment

Large Language Models & Transformers

  • LLMs for code generation and developer productivity
  • Transformer architectures in JavaScript/Node.js environments
  • Multi-agent systems and agent communication protocols
  • Context window management and optimization strategies

Financial Technology & Algorithmic Trading

  • High-frequency trading system architecture
  • Algorithmic trading strategies and systematic trading systems
  • Portfolio optimization using Monte Carlo methods and neural networks
  • Deep reinforcement learning for trading agents
  • ARIMA vs LSTM models for financial forecasting
  • Systems that handle billions of dollars in daily transactions

DevOps & Continuous Delivery for AI Systems

  • Spinnaker for JavaScript and AI application deployment
  • Canary analysis and deployment strategies for ML models
  • Fleet-wide delivery orchestration and automation
  • CI/CD pipeline optimization for AI/ML workflows

Technology Education & Accessibility

  • Making AI and programming education accessible to underrepresented groups
  • Collaborative learning models for coding education
  • Teaching methodologies for complex technical topics
  • Community-driven tech education at scale

Publications

Book

"Learn Algorithmic Trading with Python: Build Automated Electronic Trading Systems Using Python" (Apress)

Comprehensive guide to building automated trading systems covering algorithmic trading strategies, backtesting, and system architecture with practical implementations using Python.

Open Source

"TensorFlow.js Quickstart Guide"

Self-published, open-sourced introduction to deep learning and neural networks in the browser, available on GitHub.

Conference Presentations

See the full list on the Speaking page.