LinkedIn Profile I am a Master of Science in Computational Finance student at Carnegie Mellon University, with an academic background in Economics and Computer Science from Boston College. I am interested in the way markets transform uncertainty into measurable risk: how prices react to information, how models behave across regimes, and how quantitative tools can support better trading and investment decisions. My experience spans quantitative finance, investment management, machine learning, and applied economic research. At Hyde Park Investment Services, I worked on systematic equity strategy development and backtesting across S&P 500 stocks. In a volatility-timed factor strategy project, I built a Python pipeline for signal generation, portfolio construction, and performance evaluation, focusing especially on downside protection and regime sensitivity. I have also studied inflation dynamics through VAR-based research and applied ML models to financial market classification. I bring a practical, cross-disciplinary approach: economic intuition, programming discipline, and risk-focused thinking. What makes my perspective unique is that I enjoy not only building models, but also questioning their robustness, interpreting noisy signals carefully, and turning complex market behavior into structured, testable frameworks. A lens doesn’t create information—it focuses it. My training in optoelectronic engineering has taught me a way of thinking: find the signal, strip away the noise, and let the pattern speak. As I transitioned from analyzing distorted optical waveforms to modeling financial markets, I realized that the same mindset underlies quantitative investing: extracting meaningful insights from complex, noisy data to support systematic investment decisions.
