About Me
I began trading when I was eight years old— starting with equities and eventually moving into advanced options strategies focused on volatility, skew, and delta-neutral positioning. I studied Applied Mathematics at Columbia, where I built deep learning models and developed NLP systems using recurrent neural networks.
Early in my career, I worked in institutional finance. I started in private equity and alternatives at State Street, and later moved into data and risk at Apollo. These roles gave me a detailed understanding of how capital, risk, and information move through financial systems.
I eventually returned to derivatives trading full-time, specializing in volatility-driven strategies. My background in quantitative finance, applied mathematics, and machine learning continues to shape the work I do today at my startup, HedgeOS.
Interests
My work sits at the intersection of mathematics, AI, and quantitative finance— disciplines grounded in structure, reasoning, and measurable intelligence.
At the same time, I’m deeply drawn to Vedanta, consciousness studies, and quantum physics, where ancient philosophy and modern science converge to explore how humans think, perceive, and make choices. My curiosity spans both the computational and the contemplative, because truly understanding human decision-making requires both.
In parallel, I’m passionate about animal conservation and planetary restoration, with a particular interest in technologies such as 3D-printed coral reefs that help rebuild fragile ecosystems.