LinkedIn Profile In an era where decisions are increasingly built on probabilities, I strive to approach uncertainty by thinking scientifically. That is what brought me to quantitative research. I like the research process of forming a view, testing it against data, and using logic to move closer to a quality answer. I am an incoming Carnegie Mellon MSCF student interested in quantitative research, systematic trading, machine learning, and alpha research. To me, markets are moving puzzles. They are shaped by noise, incentives, and information that may or may not matter. Finding a signal in that complexity is the kind of challenge I want to work on. I have built my foundation through six internships in software engineering and quant development, including Google, Squarepoint, TD Bank, and Sov.ai. I have also held four academic research assistant roles involving factor models, volatility modeling, machine learning, and bank-market analysis. These experiences taught me how to build reliable systems, work with messy data, and test ideas by seeing where they fail. My undergraduate dual degree in Software Engineering and Business shaped my ability to build new things, communicate ideas, and bring clarity to ambiguity. If you are working on systematic strategies, alpha research, or market problems where better questions can uncover better signals, I would like to connect. I am also happy to talk over basketball, poker, chess, or a new idea worth testing.
