Building sophisticated models that transform market volatility into actionable insights. Proven track record of delivering alpha through advanced machine learning and rigorous quantitative research.
BS Computer Science, Georgia Institute of Technology, US
Concentrations: Modeling & Simulation, Theory
Combining deep theoretical knowledge with practical implementation experience to solve complex financial challenges
Expert in GARCH models, stochastic volatility, and implied volatility surfaces. Developed production systems achieving 95%+ accuracy in volatility forecasting.
Building state-of-the-art ML pipelines for alpha generation, risk prediction, and market microstructure analysis using modern deep learning architectures.
End-to-end design and implementation of low-latency trading systems with robust risk management and real-time portfolio optimization.
Comprehensive risk framework development including VaR, CVaR, stress testing, and scenario analysis for multi-asset portfolios.
Data-driven research translating academic theory into profitable trading strategies with rigorous statistical validation.
Building scalable, maintainable ML systems from research to production with modern MLOps practices and monitoring.
Proven track record across leading financial institutions and innovative fintech startups
Leading volatility modeling initiatives for equity derivatives desk managing $50M+ AUM. Developed proprietary ML models for volatility surface construction and options pricing.
Developed production ML systems for market making and trade execution optimization. Focused on low-latency inference and real-time portfolio risk management.
Research and development of statistical arbitrage strategies across global equity markets. Built and maintained production trading systems with rigorous backtesting frameworks.
Explored deep learning applications for market microstructure prediction. Prototyped novel architectures for order book dynamics modeling.
Open-source contributions and research demonstrating cutting-edge quantitative techniques
Production-grade Python library for volatility modeling using GARCH, EGARCH, and neural network architectures. Includes comprehensive backtesting framework and visualization tools.
Deep learning models for limit order book dynamics. Implements CNN, LSTM, and Transformer architectures for high-frequency price movement prediction.
High-performance C++ library for options pricing and Greeks calculation. Supports Black-Scholes, binomial trees, and Monte Carlo methods with Python bindings.
Event-driven backtesting engine with realistic market simulation, transaction costs, and slippage modeling. Includes portfolio analytics and performance attribution.
Published research on using neural networks for arbitrage-free volatility surface construction. Demonstrates superior performance vs. traditional parametric methods on S&P 500 options.
Legally authorized to reside and work in the European Union, with the ability to collaborate either as a full-time employee or as a self-employed contractor. Operates as a registered self-employed professional in Portugal and is based in Coimbra.