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My work focuses on the design, testing, and
optimization of systematic trading strategies
grounded in quantitative methods. I am
particularly interested in building models that
are robust, measurable, and capable of
operating across different market conditions.
Rather than relying on discretionary decision-
making, my approach emphasizes data‑driven
logic, empirical validation, and rigorous
performance evaluation.

My background spans business, supply chain,
and financial markets, giving me a practical
understanding of economic mechanisms in
addition to quantitative finance. This
cross‑disciplinary perspective allows me to
approach markets not only from a theoretical
standpoint, but also with a concrete view of
real‑world constraints, costs, and execution.

At Full AI Lab, I work on research and
development projects related to algorithmic
strategies, backtesting frameworks, and
market modeling. A significant part of my
research focuses on understanding market
dynamics through quantitative analysis,
including strategy optimization, risk
management, and the exploration of advanced
topics such as market microstructure and
order‑flow dynamics.

Innovation is a central driver of my work. I
continuously explore new methodologies,
challenge existing assumptions, and refine
models through experimentation and iteration.
My objective is to contribute to the
development of systematic, scalable, and
well‑founded quantitative approaches to
financial markets.