Playing poker taught me that making the right decision does not always mean knowing the definitive answer. When operating with incomplete information and strict time constraints, the core challenge lies in sizing bets appropriately based on one's hand and opponent dynamics. That mindset drew me to quantitative finance, where I became fascinated by applying probability, statistical data, and mathematical models to evaluate risk and optimize trading decisions. I am pursuing opportunities in quantitative research and trading to develop strategies supporting systematic and semi-systematic investment processes. I specialize in extracting signals from high-dimensional or noisy datasets and rigorous backtesting to evaluate signal robustness under shifting market regimes. Throughout my academic background, I have consistently applied this methodology to complex quantitative problems. As the lead for a 15-person computer vision research team, I analyzed how parameter estimations behaved under image distortion and poor framing. At Segal, I built regression models on a dataset of over two million healthcare records, leveraging sparse matrix techniques to substantially reduce memory footprints and computational overhead. Additionally, I implemented Monte Carlo simulations in Python to model hedging P&L and analyze market frictions within the Heston stochastic volatility framework. I would welcome the opportunity to connect and discuss how my quantitative background and analytical skills align with your team's goals.
