Quantitative Research

Quantitative researcher focused on systematic trading, market intelligence, and risk-aware portfolio construction

I build research-to-execution pipelines across signal generation, backtesting, risk control, and monitoring, with experience spanning crypto, equities, market microstructure, and NLP-driven trading research.

I am a Financial Mathematics graduate student at Johns Hopkins University with experience in systematic trading, quantitative research, and market microstructure. My work combines machine learning, reinforcement learning, NLP, and statistical modeling to build disciplined trading systems under real-world constraints. I have worked across crypto and equity strategies, factor research, investor-facing performance reporting, and execution-aware research pipelines.