About Spencer Ozgur
I am a Master of Science student in Financial Engineering at Columbia University in the City of New York. I am deeply passionate about exploring how machine learning models are transforming stock market dynamics and driving modern investment strategies. My goal is to bridge the gap between quantitative research and trading by leveraging these insights to optimize decision-making and maximize returns. Through my work, I aim to develop cutting-edge financial models that empower traders with data-driven strategies in an increasingly algorithmic market.
Academic Journey
My journey began at Arizona State University, where I studied Computer Science with a focus on how machines could reason and learn. That curiosity led me to LabV2, where I conducted AI research under Professor Paulo Shakarian, exploring the intersection of symbolic logic and machine learning with applications in complex, adversarial environments.
As my technical foundation deepened, I grew increasingly drawn to quantitative finance — a field where mathematics, computation, and markets converge. I wanted to build systems that didn't just model the world but operated within it in real time.
That pursuit brought me to Columbia University, where I am completing a Master's in Financial Engineering with a concentration in Machine Learning in Finance. At Columbia, I have worked on projects spanning high-frequency market making, mean-field game theory for optimal execution, and state-space filtering methods for macroeconomic modeling — all grounded in the same question I started with at ASU: how do you get a system to make good decisions under uncertainty?
I graduate in December 2026 and am targeting quantitative developer and research roles at the intersection of systematic strategies, market microstructure, and algorithmic trading.
Education
Columbia University
New York, NY
December 2026
Master of Science in Financial Engineering
Arizona State University
Tempe, AZ
December 2024
Bachelor of Science in Computer Science (Minor in Mathematics)
