I’m building a profile at the intersection of mathematics, quantitative research, and systematic investment strategies.

Currently, my work moves across three layers:

• understanding the mathematical structure behind stochastic systems
• testing whether investment signals contain real information
• and translating signals into portfolio decisions without pretending that backtests are truth

A proof-driven mathematical research lab on random walks, martingales, stopping times, quadratic variation, and convergence toward Brownian motion. This repo is the mathematical foundation layer: probability, stochastic processes, and the kind of reasoning that sits underneath serious quantitative finance.

A research toolkit for evaluating systematic alpha signals. The focus is not on building another random backtest, but on understanding signal quality through Rank IC, decay, quantile spreads, robustness checks, and information stability.

A portfolio construction framework that translates ranked signals into constrained allocations. The project connects research signals with practical implementation questions such as turnover, exposure control, position sizing, and risk-aware portfolio formation.

I am also involved in FVN Research, where I work on systematic alpha research notes and market-oriented research outputs. In parallel, I am active on WorldQuant BRAIN / IQC, where I develop and test systematic alpha ideas under platform constraints.

Together, these projects form my current research direction:

Mathematical foundations → alpha research → portfolio implementation.

I am especially interested in the space where mathematics becomes useful without becoming decorative.

Areas I’m currently exploring:

• Stochastic Processes
• Probability Theory
• Martingales
• Systematic Alpha Research
• Empirical Asset Pricing
• Portfolio Construction
• Treatment Effects and Causal Inference
• Quantitative Research Infrastructure